A significant number of hotel bookings are called off due to cancellations or no-shows. The typical reasons for cancellations include change of plans, scheduling conflicts, etc. This is often made easier by the option to do so free of charge or preferably at a low cost which is beneficial to hotel guests but it is a less desirable and possibly revenue-diminishing factor for hotels to deal with. Such losses are particularly high on last-minute cancellations.
The new technologies involving online booking channels have dramatically changed customers’ booking possibilities and behavior. This adds a further dimension to the challenge of how hotels handle cancellations, which are no longer limited to traditional booking and guest characteristics.
The cancellation of bookings impact a hotel on various fronts:
The increasing number of cancellations calls for a Machine Learning based solution that can help in predicting which booking is likely to be canceled. INN Hotels Group has a chain of hotels in Portugal, they are facing problems with the high number of booking cancellations and have reached out to your firm for data-driven solutions. You as a data scientist have to analyze the data provided to find which factors have a high influence on booking cancellations, build a predictive model that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.
The data contains the different attributes of customers' booking details:
# Ignore warnings
import warnings as wns
wns.filterwarnings('ignore')
# import the important packages
# library used for data manipulation and analysis
import pandas as pd
# library used for working with arrays.
import numpy as np
# library for plots and visualisations
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
# Removing the limit of number of columns and rows printed
pd.set_option('display.max_columns',None) # Display all columns
pd.set_option('display.max_rows',200)# Display 200 rows.
#Parser engine to use. The C engine is faster while the python engine is currently more feature-complete.
url = "INNHotelsGroup.csv"
Hotel_df = pd.read_csv(url, engine="python")
# Get the shape of the data.
print(f'The INN Hotel Booking dataframe has {Hotel_df.shape[0]} rows and {Hotel_df.shape[1]} columns.')
The INN Hotel Booking dataframe has 36275 rows and 19 columns.
# Get a sample 10 rows of data using random state = 1
Hotel_df.sample(n=10, random_state=1)
| Booking_ID | no_of_adults | no_of_children | no_of_weekend_nights | no_of_week_nights | type_of_meal_plan | required_car_parking_space | room_type_reserved | lead_time | arrival_year | arrival_month | arrival_date | market_segment_type | repeated_guest | no_of_previous_cancellations | no_of_previous_bookings_not_canceled | avg_price_per_room | no_of_special_requests | booking_status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 30392 | INN30393 | 1 | 0 | 1 | 0 | Not Selected | 0 | Room_Type 1 | 53 | 2018 | 9 | 11 | Online | 0 | 0 | 0 | 94.32 | 0 | Not_Canceled |
| 6685 | INN06686 | 2 | 0 | 1 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 63 | 2018 | 4 | 22 | Online | 0 | 0 | 0 | 105.30 | 1 | Canceled |
| 8369 | INN08370 | 2 | 0 | 2 | 3 | Meal Plan 1 | 0 | Room_Type 4 | 55 | 2018 | 9 | 11 | Online | 0 | 0 | 0 | 106.24 | 0 | Not_Canceled |
| 2055 | INN02056 | 2 | 0 | 0 | 2 | Not Selected | 0 | Room_Type 1 | 53 | 2017 | 12 | 29 | Online | 0 | 0 | 0 | 81.00 | 1 | Not_Canceled |
| 10969 | INN10970 | 1 | 0 | 2 | 4 | Meal Plan 1 | 0 | Room_Type 1 | 245 | 2018 | 7 | 6 | Offline | 0 | 0 | 0 | 110.00 | 0 | Canceled |
| 24881 | INN24882 | 2 | 0 | 3 | 7 | Meal Plan 1 | 0 | Room_Type 2 | 231 | 2018 | 8 | 1 | Online | 0 | 0 | 0 | 81.82 | 2 | Canceled |
| 28658 | INN28659 | 2 | 0 | 0 | 3 | Meal Plan 2 | 0 | Room_Type 1 | 71 | 2018 | 5 | 10 | Offline | 0 | 0 | 0 | 126.00 | 1 | Not_Canceled |
| 20853 | INN20854 | 2 | 0 | 1 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 66 | 2017 | 10 | 9 | Offline | 0 | 0 | 0 | 75.00 | 0 | Canceled |
| 8501 | INN08502 | 2 | 0 | 0 | 3 | Meal Plan 1 | 0 | Room_Type 2 | 40 | 2018 | 1 | 14 | Online | 0 | 0 | 0 | 77.55 | 1 | Not_Canceled |
| 1942 | INN01943 | 2 | 0 | 0 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 63 | 2018 | 8 | 9 | Online | 0 | 0 | 0 | 144.90 | 2 | Not_Canceled |
The Hotel Booking DataFrame has 19 columns as mentioned in the Data Dictionary. Data in each row corresponds to the details of each booking, both cancelled and not-cancelled.
# use info() method to print a concise summary of the pandas DataFrame
Hotel_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 36275 entries, 0 to 36274 Data columns (total 19 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Booking_ID 36275 non-null object 1 no_of_adults 36275 non-null int64 2 no_of_children 36275 non-null int64 3 no_of_weekend_nights 36275 non-null int64 4 no_of_week_nights 36275 non-null int64 5 type_of_meal_plan 36275 non-null object 6 required_car_parking_space 36275 non-null int64 7 room_type_reserved 36275 non-null object 8 lead_time 36275 non-null int64 9 arrival_year 36275 non-null int64 10 arrival_month 36275 non-null int64 11 arrival_date 36275 non-null int64 12 market_segment_type 36275 non-null object 13 repeated_guest 36275 non-null int64 14 no_of_previous_cancellations 36275 non-null int64 15 no_of_previous_bookings_not_canceled 36275 non-null int64 16 avg_price_per_room 36275 non-null float64 17 no_of_special_requests 36275 non-null int64 18 booking_status 36275 non-null object dtypes: float64(1), int64(13), object(5) memory usage: 5.3+ MB
# using the describe() menthod to get the 5 point summary of the numerical columns in the dataframe.
# .T will transponse the table into a readable format with dataframe columns arranged in rows.
Hotel_df.describe().T
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| no_of_adults | 36275.0 | 1.844962 | 0.518715 | 0.0 | 2.0 | 2.00 | 2.0 | 4.0 |
| no_of_children | 36275.0 | 0.105279 | 0.402648 | 0.0 | 0.0 | 0.00 | 0.0 | 10.0 |
| no_of_weekend_nights | 36275.0 | 0.810724 | 0.870644 | 0.0 | 0.0 | 1.00 | 2.0 | 7.0 |
| no_of_week_nights | 36275.0 | 2.204300 | 1.410905 | 0.0 | 1.0 | 2.00 | 3.0 | 17.0 |
| required_car_parking_space | 36275.0 | 0.030986 | 0.173281 | 0.0 | 0.0 | 0.00 | 0.0 | 1.0 |
| lead_time | 36275.0 | 85.232557 | 85.930817 | 0.0 | 17.0 | 57.00 | 126.0 | 443.0 |
| arrival_year | 36275.0 | 2017.820427 | 0.383836 | 2017.0 | 2018.0 | 2018.00 | 2018.0 | 2018.0 |
| arrival_month | 36275.0 | 7.423653 | 3.069894 | 1.0 | 5.0 | 8.00 | 10.0 | 12.0 |
| arrival_date | 36275.0 | 15.596995 | 8.740447 | 1.0 | 8.0 | 16.00 | 23.0 | 31.0 |
| repeated_guest | 36275.0 | 0.025637 | 0.158053 | 0.0 | 0.0 | 0.00 | 0.0 | 1.0 |
| no_of_previous_cancellations | 36275.0 | 0.023349 | 0.368331 | 0.0 | 0.0 | 0.00 | 0.0 | 13.0 |
| no_of_previous_bookings_not_canceled | 36275.0 | 0.153411 | 1.754171 | 0.0 | 0.0 | 0.00 | 0.0 | 58.0 |
| avg_price_per_room | 36275.0 | 103.423539 | 35.089424 | 0.0 | 80.3 | 99.45 | 120.0 | 540.0 |
| no_of_special_requests | 36275.0 | 0.619655 | 0.786236 | 0.0 | 0.0 | 0.00 | 1.0 | 5.0 |
This is the 5 point summary of 14 numerical columns.
# using the describe() menthod to get the 5 point summary
# using 'include' to inculde "object" and "category" columns in the dataframe.
# .T will transponse the table into a readable format with dataframe columns arranged in rows.
Hotel_df.describe(include='object').T
| count | unique | top | freq | |
|---|---|---|---|---|
| Booking_ID | 36275 | 36275 | INN05595 | 1 |
| type_of_meal_plan | 36275 | 4 | Meal Plan 1 | 27835 |
| room_type_reserved | 36275 | 7 | Room_Type 1 | 28130 |
| market_segment_type | 36275 | 5 | Online | 23214 |
| booking_status | 36275 | 2 | Not_Canceled | 24390 |
# let's create a copy of the data to avoid any changes to original data
Hotel_Data = Hotel_df.copy()
# 'Booking_ID' is unique for all rows and is not significant for further analysis.
Hotel_Data.drop(columns="Booking_ID",axis=1,inplace=True)
There are 14 columns with a numeric data type, but I think only two of them ('lead_time' and 'avg_price_per_room') can be considered as continuous. Rest of the columns can be considersed as categorical.
# hand-picking the list of numerical columns in the dataset.
numeric_cols = ['lead_time','avg_price_per_room']
numeric_cols
['lead_time', 'avg_price_per_room']
# Get the list of category columns in the dataset.
category_cols = []
# for look to find the columns that are not numerical
for col in Hotel_Data.columns.to_list():
if col not in numeric_cols:
category_cols.append(col)
category_cols
['no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'type_of_meal_plan', 'required_car_parking_space', 'room_type_reserved', 'arrival_year', 'arrival_month', 'arrival_date', 'market_segment_type', 'repeated_guest', 'no_of_previous_cancellations', 'no_of_previous_bookings_not_canceled', 'no_of_special_requests', 'booking_status']
# checking for missing value
Hotel_Data.isnull().sum()
no_of_adults 0 no_of_children 0 no_of_weekend_nights 0 no_of_week_nights 0 type_of_meal_plan 0 required_car_parking_space 0 room_type_reserved 0 lead_time 0 arrival_year 0 arrival_month 0 arrival_date 0 market_segment_type 0 repeated_guest 0 no_of_previous_cancellations 0 no_of_previous_bookings_not_canceled 0 avg_price_per_room 0 no_of_special_requests 0 booking_status 0 dtype: int64
Lets look for the hotel bookings with average price for room as Zero.
# Create a dataframe for booking with price = zero.
Hotel_Data_Price_Zero = Hotel_Data[Hotel_Data['avg_price_per_room'] == 0]
# Identify the market segments with booking status for zero price
Hotel_Data_Price_Zero[['market_segment_type','booking_status']].value_counts()
market_segment_type booking_status
Complementary Not_Canceled 354
Online Not_Canceled 185
Canceled 6
dtype: int64
Lets look at the category columns to see if all the values are expected.
# Get the value counts for category columns.
for col in category_cols:
print(Hotel_Data[col].value_counts())
print("-" * 40)
2 26108 1 7695 3 2317 0 139 4 16 Name: no_of_adults, dtype: int64 ---------------------------------------- 0 33577 1 1618 2 1058 3 19 9 2 10 1 Name: no_of_children, dtype: int64 ---------------------------------------- 0 16872 1 9995 2 9071 3 153 4 129 5 34 6 20 7 1 Name: no_of_weekend_nights, dtype: int64 ---------------------------------------- 2 11444 1 9488 3 7839 4 2990 0 2387 5 1614 6 189 7 113 8 62 10 62 9 34 11 17 15 10 12 9 14 7 13 5 17 3 16 2 Name: no_of_week_nights, dtype: int64 ---------------------------------------- Meal Plan 1 27835 Not Selected 5130 Meal Plan 2 3305 Meal Plan 3 5 Name: type_of_meal_plan, dtype: int64 ---------------------------------------- 0 35151 1 1124 Name: required_car_parking_space, dtype: int64 ---------------------------------------- Room_Type 1 28130 Room_Type 4 6057 Room_Type 6 966 Room_Type 2 692 Room_Type 5 265 Room_Type 7 158 Room_Type 3 7 Name: room_type_reserved, dtype: int64 ---------------------------------------- 2018 29761 2017 6514 Name: arrival_year, dtype: int64 ---------------------------------------- 10 5317 9 4611 8 3813 6 3203 12 3021 11 2980 7 2920 4 2736 5 2598 3 2358 2 1704 1 1014 Name: arrival_month, dtype: int64 ---------------------------------------- 13 1358 17 1345 2 1331 19 1327 4 1327 16 1306 20 1281 6 1273 15 1273 18 1260 14 1242 30 1216 12 1204 8 1198 29 1190 21 1158 5 1154 25 1146 26 1146 1 1133 9 1130 28 1129 7 1110 24 1103 11 1098 3 1098 10 1089 27 1059 22 1023 23 990 31 578 Name: arrival_date, dtype: int64 ---------------------------------------- Online 23214 Offline 10528 Corporate 2017 Complementary 391 Aviation 125 Name: market_segment_type, dtype: int64 ---------------------------------------- 0 35345 1 930 Name: repeated_guest, dtype: int64 ---------------------------------------- 0 35937 1 198 2 46 3 43 11 25 5 11 4 10 13 4 6 1 Name: no_of_previous_cancellations, dtype: int64 ---------------------------------------- 0 35463 1 228 2 112 3 80 4 65 5 60 6 36 7 24 8 23 10 19 9 19 11 15 12 12 14 9 15 8 16 7 13 7 21 6 20 6 19 6 18 6 17 6 22 6 23 3 24 3 25 3 27 3 32 2 44 2 31 2 30 2 29 2 48 2 28 2 26 2 38 1 33 1 58 1 34 1 35 1 57 1 36 1 56 1 37 1 55 1 54 1 45 1 39 1 53 1 40 1 52 1 41 1 51 1 42 1 50 1 43 1 49 1 47 1 46 1 Name: no_of_previous_bookings_not_canceled, dtype: int64 ---------------------------------------- 0 19777 1 11373 2 4364 3 675 4 78 5 8 Name: no_of_special_requests, dtype: int64 ---------------------------------------- Not_Canceled 24390 Canceled 11885 Name: booking_status, dtype: int64 ----------------------------------------
Let's add a numeric column with booking status 'Not Canceled' as 0 and 'Cancelled' as 1.
# Rename the column booking_status as booking_status_desc.
Hotel_Data.rename(columns={'booking_status':'booking_status_desc'},inplace=True)
# Apply the function to update booking status 'Not Canceled' as 0 and 'Cancelled' as 1.
Hotel_Data['booking_status'] = Hotel_Data['booking_status_desc'].apply(lambda x:0 if x == "Not_Canceled" else 1)
# Get a sample 10 rows of data using random state = 1
Hotel_df.sample(n=10, random_state=1)
| Booking_ID | no_of_adults | no_of_children | no_of_weekend_nights | no_of_week_nights | type_of_meal_plan | required_car_parking_space | room_type_reserved | lead_time | arrival_year | arrival_month | arrival_date | market_segment_type | repeated_guest | no_of_previous_cancellations | no_of_previous_bookings_not_canceled | avg_price_per_room | no_of_special_requests | booking_status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 30392 | INN30393 | 1 | 0 | 1 | 0 | Not Selected | 0 | Room_Type 1 | 53 | 2018 | 9 | 11 | Online | 0 | 0 | 0 | 94.32 | 0 | Not_Canceled |
| 6685 | INN06686 | 2 | 0 | 1 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 63 | 2018 | 4 | 22 | Online | 0 | 0 | 0 | 105.30 | 1 | Canceled |
| 8369 | INN08370 | 2 | 0 | 2 | 3 | Meal Plan 1 | 0 | Room_Type 4 | 55 | 2018 | 9 | 11 | Online | 0 | 0 | 0 | 106.24 | 0 | Not_Canceled |
| 2055 | INN02056 | 2 | 0 | 0 | 2 | Not Selected | 0 | Room_Type 1 | 53 | 2017 | 12 | 29 | Online | 0 | 0 | 0 | 81.00 | 1 | Not_Canceled |
| 10969 | INN10970 | 1 | 0 | 2 | 4 | Meal Plan 1 | 0 | Room_Type 1 | 245 | 2018 | 7 | 6 | Offline | 0 | 0 | 0 | 110.00 | 0 | Canceled |
| 24881 | INN24882 | 2 | 0 | 3 | 7 | Meal Plan 1 | 0 | Room_Type 2 | 231 | 2018 | 8 | 1 | Online | 0 | 0 | 0 | 81.82 | 2 | Canceled |
| 28658 | INN28659 | 2 | 0 | 0 | 3 | Meal Plan 2 | 0 | Room_Type 1 | 71 | 2018 | 5 | 10 | Offline | 0 | 0 | 0 | 126.00 | 1 | Not_Canceled |
| 20853 | INN20854 | 2 | 0 | 1 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 66 | 2017 | 10 | 9 | Offline | 0 | 0 | 0 | 75.00 | 0 | Canceled |
| 8501 | INN08502 | 2 | 0 | 0 | 3 | Meal Plan 1 | 0 | Room_Type 2 | 40 | 2018 | 1 | 14 | Online | 0 | 0 | 0 | 77.55 | 1 | Not_Canceled |
| 1942 | INN01943 | 2 | 0 | 0 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 63 | 2018 | 8 | 9 | Online | 0 | 0 | 0 | 144.90 | 2 | Not_Canceled |
# function to plot a boxplot and a histogram along the same scale.
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (12,7))
kde: whether to the show density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a star will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
# for col in numeric_cols:
# print("# Display a histogram and boxplot with kde curve for the numeric column '{0}'".format(col))
# print("histogram_boxplot(Hotel_Data,{0},kde=True)".format(col))
# print("-" * 40)
# function to create labeled barplots
def labeled_barplot(data, feature, perc=False, n=None):
"""
Barplot with percentage at the top
data: dataframe
feature: dataframe column
perc: whether to display percentages instead of count (default is False)
n: displays the top n category levels (default is None, i.e., display all levels)
"""
total = len(data[feature]) # length of the column
count = data[feature].nunique()
if n is None:
plt.figure(figsize=(count + 1, 5))
else:
plt.figure(figsize=(n + 1, 5))
plt.xticks(rotation=60, fontsize=12)
ax = sns.countplot(
data=data,
x=feature,
palette="Paired",
order=data[feature].value_counts().index[:n].sort_values(),
)
for p in ax.patches:
if perc == True:
label = "{:.2f}%".format(
100 * p.get_height() / total
) # percentage of each class of the category
else:
label = p.get_height() # count of each level of the category
x = p.get_x() + p.get_width() / 2 # width of the plot
y = p.get_height() # height of the plot
ax.annotate(
label,
(x, y),
ha="center",
va="center",
size=12,
xytext=(0, 5),
textcoords="offset points",
) # annotate the percentage
plt.show() # show the plot
# Display a labeled_barplot for the categorical column 'booking_status'
labeled_barplot(Hotel_Data, 'booking_status_desc', perc=True)
# Display a labeled_barplot for the categorical column 'no_of_adults'
labeled_barplot(Hotel_Data, 'no_of_adults', perc=True)
# Display a labeled_barplot for the categorical column 'no_of_children'
labeled_barplot(Hotel_Data, 'no_of_children', perc=True)
# Display a labeled_barplot for the categorical column 'no_of_weekend_nights'
labeled_barplot(Hotel_Data, 'no_of_weekend_nights', perc=True)
# Display a labeled_barplot for the categorical column 'no_of_week_nights'
labeled_barplot(Hotel_Data, 'no_of_week_nights', perc=True,n=10)
# Display a labeled_barplot for the categorical column 'type_of_meal_plan'
labeled_barplot(Hotel_Data, 'type_of_meal_plan', perc=True)
# Display a labeled_barplot for the categorical column 'required_car_parking_space'
labeled_barplot(Hotel_Data, 'required_car_parking_space', perc=True)
# Display a labeled_barplot for the categorical column 'room_type_reserved'
labeled_barplot(Hotel_Data, 'room_type_reserved', perc=True)
# Display a histogram and boxplot with kde curve for the numeric column 'lead_time'
histogram_boxplot(Hotel_Data,'lead_time',kde=True)
# Display a labeled_barplot for the categorical column 'arrival_year'
labeled_barplot(Hotel_Data, 'arrival_year', perc=True)
# Display a labeled_barplot for the categorical column 'arrival_month'
labeled_barplot(Hotel_Data, 'arrival_month', perc=True)
# Display a labeled_barplot for the categorical column 'arrival_date' (Top 12 days)
labeled_barplot(Hotel_Data, 'arrival_date', perc=True,n=12)
# Display a labeled_barplot for the categorical column 'market_segment_type'
labeled_barplot(Hotel_Data, 'market_segment_type', perc=True)
# Display a labeled_barplot for the categorical column 'repeated_guest'
labeled_barplot(Hotel_Data, 'repeated_guest', perc=True)
# Display a labeled_barplot for the categorical column 'no_of_previous_cancellations'
labeled_barplot(Hotel_Data, 'no_of_previous_cancellations', perc=True)
# Display a labeled_barplot for the categorical column 'no_of_previous_bookings_not_canceled'
labeled_barplot(Hotel_Data, 'no_of_previous_bookings_not_canceled', perc=True,n=10)
# Display a histogram and boxplot with kde curve for the numeric column 'avg_price_per_room'
histogram_boxplot(Hotel_Data,'avg_price_per_room',kde=True,bins=50)
# Display a labeled_barplot for the categorical column 'no_of_special_requests'
labeled_barplot(Hotel_Data, 'no_of_special_requests', perc=True,n=10)
# Display a pair plot to find the relationship between numerical fields.
plt.figure(figsize=(20,30))
sns.pairplot(Hotel_Data,hue='booking_status')
# Set the title for the plot
plt.title('Pair plot for numerical fields')
plt.show()
<Figure size 1440x2160 with 0 Axes>
# Print the correlation between numerical columns.
Hotel_Data.corr().round(3)
| no_of_adults | no_of_children | no_of_weekend_nights | no_of_week_nights | required_car_parking_space | lead_time | arrival_year | arrival_month | arrival_date | repeated_guest | no_of_previous_cancellations | no_of_previous_bookings_not_canceled | avg_price_per_room | no_of_special_requests | booking_status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| no_of_adults | 1.000 | -0.020 | 0.103 | 0.106 | 0.011 | 0.097 | 0.077 | 0.022 | 0.026 | -0.192 | -0.047 | -0.119 | 0.297 | 0.189 | 0.087 |
| no_of_children | -0.020 | 1.000 | 0.029 | 0.024 | 0.034 | -0.047 | 0.046 | -0.003 | 0.025 | -0.036 | -0.016 | -0.021 | 0.338 | 0.124 | 0.033 |
| no_of_weekend_nights | 0.103 | 0.029 | 1.000 | 0.180 | -0.031 | 0.047 | 0.055 | -0.010 | 0.027 | -0.067 | -0.021 | -0.026 | -0.005 | 0.061 | 0.062 |
| no_of_week_nights | 0.106 | 0.024 | 0.180 | 1.000 | -0.049 | 0.150 | 0.033 | 0.037 | -0.009 | -0.100 | -0.030 | -0.049 | 0.023 | 0.046 | 0.093 |
| required_car_parking_space | 0.011 | 0.034 | -0.031 | -0.049 | 1.000 | -0.066 | 0.016 | -0.016 | -0.000 | 0.111 | 0.027 | 0.064 | 0.061 | 0.088 | -0.086 |
| lead_time | 0.097 | -0.047 | 0.047 | 0.150 | -0.066 | 1.000 | 0.143 | 0.137 | 0.006 | -0.136 | -0.046 | -0.078 | -0.063 | -0.102 | 0.439 |
| arrival_year | 0.077 | 0.046 | 0.055 | 0.033 | 0.016 | 0.143 | 1.000 | -0.340 | 0.019 | -0.018 | 0.004 | 0.026 | 0.179 | 0.053 | 0.180 |
| arrival_month | 0.022 | -0.003 | -0.010 | 0.037 | -0.016 | 0.137 | -0.340 | 1.000 | -0.043 | 0.000 | -0.039 | -0.011 | 0.054 | 0.111 | -0.011 |
| arrival_date | 0.026 | 0.025 | 0.027 | -0.009 | -0.000 | 0.006 | 0.019 | -0.043 | 1.000 | -0.016 | -0.013 | -0.001 | 0.018 | 0.018 | 0.011 |
| repeated_guest | -0.192 | -0.036 | -0.067 | -0.100 | 0.111 | -0.136 | -0.018 | 0.000 | -0.016 | 1.000 | 0.391 | 0.539 | -0.175 | -0.012 | -0.107 |
| no_of_previous_cancellations | -0.047 | -0.016 | -0.021 | -0.030 | 0.027 | -0.046 | 0.004 | -0.039 | -0.013 | 0.391 | 1.000 | 0.468 | -0.063 | -0.003 | -0.034 |
| no_of_previous_bookings_not_canceled | -0.119 | -0.021 | -0.026 | -0.049 | 0.064 | -0.078 | 0.026 | -0.011 | -0.001 | 0.539 | 0.468 | 1.000 | -0.114 | 0.027 | -0.060 |
| avg_price_per_room | 0.297 | 0.338 | -0.005 | 0.023 | 0.061 | -0.063 | 0.179 | 0.054 | 0.018 | -0.175 | -0.063 | -0.114 | 1.000 | 0.184 | 0.143 |
| no_of_special_requests | 0.189 | 0.124 | 0.061 | 0.046 | 0.088 | -0.102 | 0.053 | 0.111 | 0.018 | -0.012 | -0.003 | 0.027 | 0.184 | 1.000 | -0.253 |
| booking_status | 0.087 | 0.033 | 0.062 | 0.093 | -0.086 | 0.439 | 0.180 | -0.011 | 0.011 | -0.107 | -0.034 | -0.060 | 0.143 | -0.253 | 1.000 |
# Set the figure size and title for the plot
plt.figure(figsize=(12,12))
plt.title('Heat map for numerical fields')
# Display a heat map to find the relationship between numerical fields.
sns.heatmap(Hotel_Data.corr(),annot=True,linewidths=0.2,fmt=".2f",cmap="YlGnBu")
plt.show()
### function to plot distributions wrt target
def distribution_plot_wrt_target(data, predictor, target):
fig, axs = plt.subplots(2, 2, figsize=(12, 10))
target_uniq = data[target].unique()
axs[0, 0].set_title("Distribution of target for target=" + str(target_uniq[0]))
sns.histplot(
data=data[data[target] == target_uniq[0]],
x=predictor,
kde=True,
ax=axs[0, 0],
color="teal",
stat="density",
)
axs[0, 1].set_title("Distribution of target for target=" + str(target_uniq[1]))
sns.histplot(
data=data[data[target] == target_uniq[1]],
x=predictor,
kde=True,
ax=axs[0, 1],
color="orange",
stat="density",
)
axs[1, 0].set_title("Boxplot w.r.t target")
sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0], palette="gist_rainbow")
axs[1, 1].set_title("Boxplot (without outliers) w.r.t target")
sns.boxplot(
data=data,
x=target,
y=predictor,
ax=axs[1, 1],
showfliers=False,
palette="gist_rainbow",
)
plt.tight_layout()
plt.show()
# function to plot stacked bar chart
def stacked_barplot(data, predictor, target):
"""
Print the category counts and plot a stacked bar chart
data: dataframe
predictor: independent variable
target: target variable
"""
count = data[predictor].nunique()
sorter = data[target].value_counts().index[-1]
tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(
by=sorter, ascending=False
)
print(tab1.head(10))
print("-" * 120)
tab = pd.crosstab(data[predictor], data[target], normalize="index").sort_values(
by=sorter, ascending=False
)
tab = tab.head(10)
if count > 10:
count = 10
tab.plot(kind="bar", stacked=True, figsize=(count + 5, 6))
plt.legend(
loc="lower left", frameon=False,
)
plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
plt.show()
# for col in category_cols:
# if col != 'booking_status':
# print("# This is the stacked barplot for {} with target as Booking status".format(col))
# print("stacked_barplot(Hotel_Data, '{}', 'booking_status_desc')".format(col))
# print("-" * 40)
# This is the distribution histplot for lead_time with target as Booking status
distribution_plot_wrt_target(Hotel_Data, 'lead_time', 'booking_status_desc')
# This is the distribution histplot for avg_price_per_room with target as Booking status
distribution_plot_wrt_target(Hotel_Data, 'avg_price_per_room', 'booking_status_desc')
# This is the stacked barplot for no_of_adults with target as Booking status
stacked_barplot(Hotel_Data, 'no_of_adults', 'booking_status_desc')
booking_status_desc Canceled Not_Canceled All no_of_adults All 11885 24390 36275 2 9119 16989 26108 1 1856 5839 7695 3 863 1454 2317 0 44 95 139 4 3 13 16 ------------------------------------------------------------------------------------------------------------------------
# This is the stacked barplot for no_of_children with target as Booking status
stacked_barplot(Hotel_Data, 'no_of_children', 'booking_status_desc')
booking_status_desc Canceled Not_Canceled All no_of_children All 11885 24390 36275 0 10882 22695 33577 1 540 1078 1618 2 457 601 1058 3 5 14 19 9 1 1 2 10 0 1 1 ------------------------------------------------------------------------------------------------------------------------
# This is the stacked barplot for no_of_weekend_nights with target as Booking status
stacked_barplot(Hotel_Data, 'no_of_weekend_nights', 'booking_status_desc')
booking_status_desc Canceled Not_Canceled All no_of_weekend_nights All 11885 24390 36275 0 5093 11779 16872 1 3432 6563 9995 2 3157 5914 9071 4 83 46 129 3 74 79 153 5 29 5 34 6 16 4 20 7 1 0 1 ------------------------------------------------------------------------------------------------------------------------
# This is the stacked barplot for no_of_week_nights with target as Booking status
stacked_barplot(Hotel_Data, 'no_of_week_nights', 'booking_status_desc')
booking_status_desc Canceled Not_Canceled All no_of_week_nights All 11885 24390 36275 2 3997 7447 11444 3 2574 5265 7839 1 2572 6916 9488 4 1143 1847 2990 0 679 1708 2387 5 632 982 1614 6 88 101 189 10 53 9 62 7 52 61 113 ------------------------------------------------------------------------------------------------------------------------
# This is the stacked barplot for type_of_meal_plan with target as Booking status
stacked_barplot(Hotel_Data, 'type_of_meal_plan', 'booking_status_desc')
booking_status_desc Canceled Not_Canceled All type_of_meal_plan All 11885 24390 36275 Meal Plan 1 8679 19156 27835 Not Selected 1699 3431 5130 Meal Plan 2 1506 1799 3305 Meal Plan 3 1 4 5 ------------------------------------------------------------------------------------------------------------------------
# This is the stacked barplot for required_car_parking_space with target as Booking status
stacked_barplot(Hotel_Data, 'required_car_parking_space', 'booking_status_desc')
booking_status_desc Canceled Not_Canceled All required_car_parking_space All 11885 24390 36275 0 11771 23380 35151 1 114 1010 1124 ------------------------------------------------------------------------------------------------------------------------
# This is the stacked barplot for room_type_reserved with target as Booking status
stacked_barplot(Hotel_Data, 'room_type_reserved', 'booking_status_desc')
booking_status_desc Canceled Not_Canceled All room_type_reserved All 11885 24390 36275 Room_Type 1 9072 19058 28130 Room_Type 4 2069 3988 6057 Room_Type 6 406 560 966 Room_Type 2 228 464 692 Room_Type 5 72 193 265 Room_Type 7 36 122 158 Room_Type 3 2 5 7 ------------------------------------------------------------------------------------------------------------------------
# This is the stacked barplot for arrival_year with target as Booking status
stacked_barplot(Hotel_Data, 'arrival_year', 'booking_status_desc')
booking_status_desc Canceled Not_Canceled All arrival_year All 11885 24390 36275 2018 10924 18837 29761 2017 961 5553 6514 ------------------------------------------------------------------------------------------------------------------------
# This is the stacked barplot for arrival_month with target as Booking status
stacked_barplot(Hotel_Data, 'arrival_month', 'booking_status_desc')
booking_status_desc Canceled Not_Canceled All arrival_month All 11885 24390 36275 10 1880 3437 5317 9 1538 3073 4611 8 1488 2325 3813 7 1314 1606 2920 6 1291 1912 3203 4 995 1741 2736 5 948 1650 2598 11 875 2105 2980 3 700 1658 2358 ------------------------------------------------------------------------------------------------------------------------
# This is the stacked barplot for arrival_date with target as Booking status
stacked_barplot(Hotel_Data, 'arrival_date', 'booking_status_desc')
booking_status_desc Canceled Not_Canceled All arrival_date All 11885 24390 36275 15 538 735 1273 4 474 853 1327 16 473 833 1306 30 465 751 1216 1 465 668 1133 12 460 744 1204 17 448 897 1345 6 444 829 1273 26 425 721 1146 ------------------------------------------------------------------------------------------------------------------------
# This is the stacked barplot for market_segment_type with target as Booking status
stacked_barplot(Hotel_Data, 'market_segment_type', 'booking_status_desc')
booking_status_desc Canceled Not_Canceled All market_segment_type All 11885 24390 36275 Online 8475 14739 23214 Offline 3153 7375 10528 Corporate 220 1797 2017 Aviation 37 88 125 Complementary 0 391 391 ------------------------------------------------------------------------------------------------------------------------
# This is the stacked barplot for repeated_guest with target as Booking status
stacked_barplot(Hotel_Data, 'repeated_guest', 'booking_status_desc')
booking_status_desc Canceled Not_Canceled All repeated_guest All 11885 24390 36275 0 11869 23476 35345 1 16 914 930 ------------------------------------------------------------------------------------------------------------------------
# This is the stacked barplot for no_of_previous_cancellations with target as Booking status
stacked_barplot(Hotel_Data, 'no_of_previous_cancellations', 'booking_status_desc')
booking_status_desc Canceled Not_Canceled All no_of_previous_cancellations All 11885 24390 36275 0 11869 24068 35937 1 11 187 198 13 4 0 4 3 1 42 43 2 0 46 46 4 0 10 10 5 0 11 11 6 0 1 1 11 0 25 25 ------------------------------------------------------------------------------------------------------------------------
# Set the figure size and title for the plot
plt.figure(figsize=(12,7))
plt.title('Bivariate analysis of the fields - booking_status and no_of_previous_bookings_not_canceled')
# Line plot to compare used cell phone price vs screen size
sns.barplot(x="booking_status_desc",y="no_of_previous_bookings_not_canceled",data=Hotel_Data)
plt.show()
# This is the stacked barplot for no_of_special_requests with target as Booking status
stacked_barplot(Hotel_Data, 'no_of_special_requests', 'booking_status_desc')
booking_status_desc Canceled Not_Canceled All no_of_special_requests All 11885 24390 36275 0 8545 11232 19777 1 2703 8670 11373 2 637 3727 4364 3 0 675 675 4 0 78 78 5 0 8 8 ------------------------------------------------------------------------------------------------------------------------
# Set the figure size and title for the plot
plt.figure(figsize=(13, 8))
plt.title('Multivariate analysis of the fields using box plot- lead_time, avg_price_per_room and booking_status')
# Scatter plot for analying the fields - used_price, days used and operating system (os)
sns.scatterplot(x="lead_time", y="avg_price_per_room",hue="booking_status",data=Hotel_Data)
plt.show()
# Set the figure size and title for the plot
plt.figure(figsize=(15,5))
plt.title('Analysing the fields - market_segment_type and lead_time - color encoded with booking_status')
# Show the pointplot with a legend - used_price and os - color encoded with 4g status
sns.pointplot(x="market_segment_type", y="lead_time", hue = 'booking_status', data=Hotel_Data)
plt.show()
# Categorical Box plot with a legend - no_of_special_requests, avg_price_per_room, booking_status and market_segment_type.
sns.catplot(x="no_of_special_requests",
y="avg_price_per_room",
hue="booking_status",
col="market_segment_type",
data=Hotel_Data,
kind="strip",
col_wrap=2
)
plt.show()
# Display a labeled_barplot for the categorical column 'arrival_month'
labeled_barplot(Hotel_Data, 'arrival_month', perc=True)
# Display a labeled_barplot for the categorical column 'market_segment_type'
labeled_barplot(Hotel_Data, 'market_segment_type', perc=True)
# Create a dataframe with the mean of average price grouped by market_segment_type and sorted in descending order.
Hotel_Data_Price_by_Seg = Hotel_Data.groupby(by='market_segment_type')['avg_price_per_room'].mean().sort_values(
ascending=False).reset_index()
Hotel_Data_Price_by_Seg
| market_segment_type | avg_price_per_room | |
|---|---|---|
| 0 | Online | 112.256855 |
| 1 | Aviation | 100.704000 |
| 2 | Offline | 91.632679 |
| 3 | Corporate | 82.911740 |
| 4 | Complementary | 3.141765 |
# Set the figure size and title for the plot
plt.figure(figsize=(8, 5))
plt.title('Analysing the fields - market_segment_type and avg_price_per_room')
# Show the bar plot - battery and brand_name
sns.barplot(data=Hotel_Data_Price_by_Seg, y="avg_price_per_room", x="market_segment_type")
plt.xticks(rotation=60,size=12)
plt.show()
# Get the total number of cancellations
No_of_cancellations = len(Hotel_Data[Hotel_Data['booking_status'] == 1])
# Get the total number of bookings
No_of_bookings = len(Hotel_Data['booking_status'])
# Get the percentage of bookings that are cancelled
print(np.round((No_of_cancellations/No_of_bookings)*100,2),'% of the bookings are cancelled')
32.76 % of the bookings are cancelled
# Display a labeled_barplot for the categorical column 'booking_status'
labeled_barplot(Hotel_Data, 'booking_status_desc', perc=True)
# Create a dataframe for repeated customers.
Hotel_Data_Rep_Guests = Hotel_Data[Hotel_Data['repeated_guest'] == 1]
# Get the total number of cancellations for repeated customers
No_of_cancellations = len(Hotel_Data_Rep_Guests[Hotel_Data_Rep_Guests['booking_status'] == 1])
# Get the total number of bookings for repeated customers
No_of_bookings = len(Hotel_Data_Rep_Guests['booking_status'])
# Get the percentage of bookings that are cancelled by repeated customers
print(np.round((No_of_cancellations/No_of_bookings)*100,2),'% of the bookings are cancelled for repeated customers.')
1.72 % of the bookings are cancelled for repeated customers.
# Display a labeled_barplot for the categorical column 'booking_status'
labeled_barplot(Hotel_Data_Rep_Guests, 'booking_status_desc', perc=True)
# Create a dataframe for customers with special requirements.
Hotel_Data_Spl_Req = Hotel_Data[Hotel_Data['no_of_special_requests'] == 0]
# Get the total number of cancellations for customers with special requirements.
No_of_cancellations = len(Hotel_Data_Spl_Req[Hotel_Data_Spl_Req['booking_status'] == 1])
# Get the total number of bookings by customers with special requirements.
No_of_bookings = len(Hotel_Data_Spl_Req['booking_status'])
# Get the percentage of bookings that are cancelled by customers with special requirements.
print(np.round((No_of_cancellations/No_of_bookings)*100,2),'% of the bookings are cancelled by customers with special requirements.')
43.21 % of the bookings are cancelled by customers with special requirements.
# Display a labeled_barplot for the categorical column 'booking_status'
labeled_barplot(Hotel_Data_Spl_Req, 'booking_status_desc', perc=True)
Let's encode the categorical columns - type_of_meal_plan, room_type_reserved and market_segment_type.
# Display the values in the column - type_of_meal_plan and room_type_reserved
print(Hotel_Data['type_of_meal_plan'].value_counts())
print(Hotel_Data['room_type_reserved'].value_counts())
Meal Plan 1 27835 Not Selected 5130 Meal Plan 2 3305 Meal Plan 3 5 Name: type_of_meal_plan, dtype: int64 Room_Type 1 28130 Room_Type 4 6057 Room_Type 6 966 Room_Type 2 692 Room_Type 5 265 Room_Type 7 158 Room_Type 3 7 Name: room_type_reserved, dtype: int64
# Create a structure to encode the column values - type_of_meal_plan and room_type_reserved
ValueReplaceStruct = {
"type_of_meal_plan":{"Not Selected":0,"Meal Plan 1":1,"Meal Plan 2":2,"Meal Plan 3":3},
"room_type_reserved":{"Room_Type 1":0,"Room_Type 2":1,"Room_Type 3":2,"Room_Type 4":3,"Room_Type 5":4,"Room_Type 6":5,"Room_Type 7":6}
}
# Assign market_segment_type to the list oneHotCols
oneHotCols=["market_segment_type"]
# Display the replace struct
ValueReplaceStruct
{'type_of_meal_plan': {'Not Selected': 0,
'Meal Plan 1': 1,
'Meal Plan 2': 2,
'Meal Plan 3': 3},
'room_type_reserved': {'Room_Type 1': 0,
'Room_Type 2': 1,
'Room_Type 3': 2,
'Room_Type 4': 3,
'Room_Type 5': 4,
'Room_Type 6': 5,
'Room_Type 7': 6}}
# Encode the column values - type_of_meal_plan and room_type_reserved
Hotel_Data.replace(ValueReplaceStruct,inplace=True)
# OneHot encode column market_segment_type
Hotel_Data=pd.get_dummies(Hotel_Data, columns=oneHotCols, drop_first=True)
# Get a sample 10 rows of data using random state = 1
Hotel_Data.sample(n=10, random_state=1)
| no_of_adults | no_of_children | no_of_weekend_nights | no_of_week_nights | type_of_meal_plan | required_car_parking_space | room_type_reserved | lead_time | arrival_year | arrival_month | arrival_date | repeated_guest | no_of_previous_cancellations | no_of_previous_bookings_not_canceled | avg_price_per_room | no_of_special_requests | booking_status_desc | booking_status | market_segment_type_Complementary | market_segment_type_Corporate | market_segment_type_Offline | market_segment_type_Online | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 30392 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 53 | 2018 | 9 | 11 | 0 | 0 | 0 | 94.32 | 0 | Not_Canceled | 0 | 0 | 0 | 0 | 1 |
| 6685 | 2 | 0 | 1 | 2 | 1 | 0 | 0 | 63 | 2018 | 4 | 22 | 0 | 0 | 0 | 105.30 | 1 | Canceled | 1 | 0 | 0 | 0 | 1 |
| 8369 | 2 | 0 | 2 | 3 | 1 | 0 | 3 | 55 | 2018 | 9 | 11 | 0 | 0 | 0 | 106.24 | 0 | Not_Canceled | 0 | 0 | 0 | 0 | 1 |
| 2055 | 2 | 0 | 0 | 2 | 0 | 0 | 0 | 53 | 2017 | 12 | 29 | 0 | 0 | 0 | 81.00 | 1 | Not_Canceled | 0 | 0 | 0 | 0 | 1 |
| 10969 | 1 | 0 | 2 | 4 | 1 | 0 | 0 | 245 | 2018 | 7 | 6 | 0 | 0 | 0 | 110.00 | 0 | Canceled | 1 | 0 | 0 | 1 | 0 |
| 24881 | 2 | 0 | 3 | 7 | 1 | 0 | 1 | 231 | 2018 | 8 | 1 | 0 | 0 | 0 | 81.82 | 2 | Canceled | 1 | 0 | 0 | 0 | 1 |
| 28658 | 2 | 0 | 0 | 3 | 2 | 0 | 0 | 71 | 2018 | 5 | 10 | 0 | 0 | 0 | 126.00 | 1 | Not_Canceled | 0 | 0 | 0 | 1 | 0 |
| 20853 | 2 | 0 | 1 | 2 | 1 | 0 | 0 | 66 | 2017 | 10 | 9 | 0 | 0 | 0 | 75.00 | 0 | Canceled | 1 | 0 | 0 | 1 | 0 |
| 8501 | 2 | 0 | 0 | 3 | 1 | 0 | 1 | 40 | 2018 | 1 | 14 | 0 | 0 | 0 | 77.55 | 1 | Not_Canceled | 0 | 0 | 0 | 0 | 1 |
| 1942 | 2 | 0 | 0 | 2 | 1 | 0 | 0 | 63 | 2018 | 8 | 9 | 0 | 0 | 0 | 144.90 | 2 | Not_Canceled | 0 | 0 | 0 | 0 | 1 |
# Display the values in the column - type_of_meal_plan and room_type_reserved
print(Hotel_Data['type_of_meal_plan'].value_counts())
print(Hotel_Data['room_type_reserved'].value_counts())
1 27835 0 5130 2 3305 3 5 Name: type_of_meal_plan, dtype: int64 0 28130 3 6057 5 966 1 692 4 265 6 158 2 7 Name: room_type_reserved, dtype: int64
# Drop the temporary column booking_status_desc as it is no longer needed in further analysis.
Hotel_Data.drop('booking_status_desc',axis=1,inplace=True)
# use info() method to print a concise summary of the pandas DataFrame
Hotel_Data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 36275 entries, 0 to 36274 Data columns (total 21 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 no_of_adults 36275 non-null int64 1 no_of_children 36275 non-null int64 2 no_of_weekend_nights 36275 non-null int64 3 no_of_week_nights 36275 non-null int64 4 type_of_meal_plan 36275 non-null int64 5 required_car_parking_space 36275 non-null int64 6 room_type_reserved 36275 non-null int64 7 lead_time 36275 non-null int64 8 arrival_year 36275 non-null int64 9 arrival_month 36275 non-null int64 10 arrival_date 36275 non-null int64 11 repeated_guest 36275 non-null int64 12 no_of_previous_cancellations 36275 non-null int64 13 no_of_previous_bookings_not_canceled 36275 non-null int64 14 avg_price_per_room 36275 non-null float64 15 no_of_special_requests 36275 non-null int64 16 booking_status 36275 non-null int64 17 market_segment_type_Complementary 36275 non-null uint8 18 market_segment_type_Corporate 36275 non-null uint8 19 market_segment_type_Offline 36275 non-null uint8 20 market_segment_type_Online 36275 non-null uint8 dtypes: float64(1), int64(16), uint8(4) memory usage: 4.8 MB
Note that all the columns have now became numeric for Model prediction.
For now, I have decided not to do outlier treatment as the models are capable of handling outliers.
# defining X and y variables
X = Hotel_Data.drop(['booking_status'],axis=1)
y = Hotel_Data['booking_status']
# Get a copy of X and y varaibles for use in Decision Tree model
X_copy = X.copy()
y_copy = y.copy()
# Library to split data
from sklearn.model_selection import train_test_split
# splitting the data in 70:30 ratio for train to test data
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.3, random_state=1
)
print("Number of rows in train data =", X_train.shape[0])
print("Number of rows in test data =", X_test.shape[0])
Number of rows in train data = 25392 Number of rows in test data = 10883
print("Original Cancelled Bookings : {0} ({1:0.2f}%)".format(len(Hotel_Data.loc[Hotel_Data['booking_status'] == 1]), (len(Hotel_Data.loc[Hotel_Data['booking_status'] == 1])/len(Hotel_Data.index)) * 100))
print("Original Non-Cancelled Bookings : {0} ({1:0.2f}%)".format(len(Hotel_Data.loc[Hotel_Data['booking_status'] == 0]), (len(Hotel_Data.loc[Hotel_Data['booking_status'] == 0])/len(Hotel_Data.index)) * 100))
print("")
print("Training Cancelled Bookings : {0} ({1:0.2f}%)".format(len(y_train[y_train[:] == 1]), (len(y_train[y_train[:] == 1])/len(y_train)) * 100))
print("Training Non-Cancelled Bookings : {0} ({1:0.2f}%)".format(len(y_train[y_train[:] == 0]), (len(y_train[y_train[:] == 0])/len(y_train)) * 100))
print("")
print("Test Cancelled Bookings : {0} ({1:0.2f}%)".format(len(y_test[y_test[:] == 1]), (len(y_test[y_test[:] == 1])/len(y_test)) * 100))
print("Test Non-Cancelled Bookings : {0} ({1:0.2f}%)".format(len(y_test[y_test[:] == 0]), (len(y_test[y_test[:] == 0])/len(y_test)) * 100))
Original Cancelled Bookings : 11885 (32.76%) Original Non-Cancelled Bookings : 24390 (67.24%) Training Cancelled Bookings : 8363 (32.94%) Training Non-Cancelled Bookings : 17029 (67.06%) Test Cancelled Bookings : 3522 (32.36%) Test Non-Cancelled Bookings : 7361 (67.64%)
There are 32.76% of cancelled bookings in original dataset. This ratio is consistent with Training and testing dataset.
First, let's create functions to calculate different metrics and confusion matrix so that we don't have to use the same code repeatedly for each model.
# defining a function to compute different metrics to check performance of a classification model built using statsmodels
def confusion_matrix_sklearn(model, predictors, target):
"""
To plot the confusion_matrix with percentages
model: classifier
predictors: independent variables
target: dependent variable
"""
y_pred = model.predict(predictors)
cm = confusion_matrix(target, y_pred)
labels = np.asarray(
[
["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
for item in cm.flatten()
]
).reshape(2, 2)
plt.figure(figsize=(6, 4))
sns.heatmap(cm, annot=labels, fmt="")
plt.ylabel("Actual label")
plt.xlabel("Predicted label")
# defining a function to compute different metrics to check performance of a classification model built using statsmodels
def model_performance_classification_sklearnmodels(
model, predictors, target
):
"""
Function to compute different metrics to check classification sklearn model performance
model: classifier
predictors: independent variables
target: dependent variable
"""
# prediction for sklearn model
pred = model.predict(predictors)
acc = accuracy_score(target, pred) # to compute Accuracy
recall = recall_score(target, pred) # to compute Recall
precision = precision_score(target, pred) # to compute Precision
f1 = f1_score(target, pred) # to compute F1-score
# creating a dataframe of metrics
df_perf = pd.DataFrame(
{"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
index=[0],
)
return df_perf
# defining a function to plot the confusion_matrix of a classification model
def confusion_matrix_statsmodels(model, predictors, target, threshold=0.5):
"""
To plot the confusion_matrix with percentages
model: classifier
predictors: independent variables
target: dependent variable
threshold: threshold for classifying the observation as class 1
"""
y_pred = model.predict(predictors) > threshold
cm = confusion_matrix(target, y_pred)
labels = np.asarray(
[
["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
for item in cm.flatten()
]
).reshape(2, 2)
plt.figure(figsize=(6, 4))
sns.heatmap(cm, annot=labels, fmt="")
plt.ylabel("Actual label")
plt.xlabel("Predicted label")
# defining a function to compute different metrics to check performance of a classification model built using statsmodels
def model_performance_classification_statsmodels(
model, predictors, target, threshold=0.5
):
"""
Function to compute different metrics to check classification model performance
model: classifier
predictors: independent variables
target: dependent variable
threshold: threshold for classifying the observation as class 1
"""
# checking which probabilities are greater than threshold
pred_temp = model.predict(predictors) > threshold
# rounding off the above values to get classes
pred = np.round(pred_temp)
acc = accuracy_score(target, pred) # to compute Accuracy
recall = recall_score(target, pred) # to compute Recall
precision = precision_score(target, pred) # to compute Precision
f1 = f1_score(target, pred) # to compute F1-score
# creating a dataframe of metrics
df_perf = pd.DataFrame(
{"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
index=[0],
)
return df_perf
Build a Logistic Regression model (with sklearn) with the training data and then check it's performance.
# To build model for prediction
from sklearn.linear_model import LogisticRegression
# To get diferent metric scores
from sklearn.metrics import (
f1_score,
accuracy_score,
recall_score,
precision_score,
confusion_matrix,
roc_auc_score,
plot_confusion_matrix,
precision_recall_curve,
roc_curve,
)
# building a Logistic Regression model.
hotel_booking_sklearn_log_reg_model = LogisticRegression(solver='newton-cg',random_state=1)
hotel_booking_sklearn_log_reg_model.fit(X_train,y_train)
LogisticRegression(random_state=1, solver='newton-cg')
# creating confusion matrix
confusion_matrix_sklearn(hotel_booking_sklearn_log_reg_model,X_train,y_train)
# checking model performance for this model
hotel_booking_sklearn_log_reg_model_train_perf_df = model_performance_classification_sklearnmodels(
hotel_booking_sklearn_log_reg_model,X_train,y_train)
print("Training performance:")
hotel_booking_sklearn_log_reg_model_train_perf_df
Training performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.803009 | 0.624298 | 0.737325 | 0.67612 |
# creating confusion matrix
confusion_matrix_sklearn(hotel_booking_sklearn_log_reg_model,X_test,y_test)
# checking model performance for this model
hotel_booking_sklearn_log_reg_model_test_perf_df = model_performance_classification_sklearnmodels(
hotel_booking_sklearn_log_reg_model,X_test,y_test)
print("Testing performance:")
hotel_booking_sklearn_log_reg_model_test_perf_df
Testing performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.804558 | 0.624645 | 0.732113 | 0.674123 |
Build a Logistic Regression model (with statsmodels) with the training data and then check it's performance.
# To build model for prediction
import statsmodels.stats.api as sms
from statsmodels.stats.outliers_influence import variance_inflation_factor
import statsmodels.api as sm
from statsmodels.tools.tools import add_constant
from statsmodels.tools.sm_exceptions import ConvergenceWarning
wns.simplefilter('ignore', ConvergenceWarning)
# adding constant
X = sm.add_constant(X)
# splitting in training and test set
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3,random_state=1)
# building a Logistic Regression model.
hotel_booking_statsmodels_log_reg_model = sm.Logit(y_train,X_train)
hotel_booking_statsmodels_log_reg_model = hotel_booking_statsmodels_log_reg_model.fit(disp=False)
# setting disp=False will remove the information on number of iterations
print(hotel_booking_statsmodels_log_reg_model.summary())
Logit Regression Results
==============================================================================
Dep. Variable: booking_status No. Observations: 25392
Model: Logit Df Residuals: 25371
Method: MLE Df Model: 20
Date: Sat, 20 Nov 2021 Pseudo R-squ.: 0.3279
Time: 00:18:40 Log-Likelihood: -10816.
converged: False LL-Null: -16091.
Covariance Type: nonrobust LLR p-value: 0.000
========================================================================================================
coef std err z P>|z| [0.025 0.975]
--------------------------------------------------------------------------------------------------------
const -892.7731 118.847 -7.512 0.000 -1125.708 -659.838
no_of_adults 0.1368 0.037 3.668 0.000 0.064 0.210
no_of_children 0.0333 0.046 0.718 0.473 -0.058 0.124
no_of_weekend_nights 0.1037 0.020 5.253 0.000 0.065 0.142
no_of_week_nights 0.0358 0.012 2.932 0.003 0.012 0.060
type_of_meal_plan -0.0842 0.041 -2.030 0.042 -0.165 -0.003
required_car_parking_space -1.6085 0.138 -11.678 0.000 -1.878 -1.339
room_type_reserved -0.1415 0.015 -9.192 0.000 -0.172 -0.111
lead_time 0.0157 0.000 59.181 0.000 0.015 0.016
arrival_year 0.4413 0.059 7.493 0.000 0.326 0.557
arrival_month -0.0415 0.006 -6.432 0.000 -0.054 -0.029
arrival_date 0.0007 0.002 0.384 0.701 -0.003 0.005
repeated_guest -2.3609 0.618 -3.819 0.000 -3.572 -1.149
no_of_previous_cancellations 0.2630 0.086 3.049 0.002 0.094 0.432
no_of_previous_bookings_not_canceled -0.1708 0.153 -1.117 0.264 -0.470 0.129
avg_price_per_room 0.0190 0.001 26.278 0.000 0.018 0.020
no_of_special_requests -1.4654 0.030 -48.859 0.000 -1.524 -1.407
market_segment_type_Complementary -23.5335 1.62e+04 -0.001 0.999 -3.18e+04 3.18e+04
market_segment_type_Corporate -1.2815 0.266 -4.827 0.000 -1.802 -0.761
market_segment_type_Offline -2.2213 0.254 -8.736 0.000 -2.720 -1.723
market_segment_type_Online -0.4188 0.251 -1.667 0.095 -0.911 0.074
========================================================================================================
But these variables might contain multicollinearity, which will affect the p-values. We will have to remove multicollinearity from the data to get reliable coefficients and p-values.
There are different ways of detecting (or testing) multi-collinearity, one such way is the Variation Inflation Factor.
Variance inflation factors measure the inflation in the variances of the regression coefficients estimates due to collinearity that exist among the predictors. It is a measure of how much the variance of the estimated regression coefficient βk is "inflated" by the existence of correlation among the predictor variables in the model.
General Rule of thumb: If VIF is 1 then there is no correlation among the kth predictor and the remaining predictor variables, and hence the variance of β̂k is not inflated at all. Whereas if VIF exceeds 5, we say there is moderate VIF and if it is 10 or exceeding 10, it shows signs of high multi-collinearity. But the purpose of the analysis should dictate which threshold to use.
# we will define a function to check VIF
def checking_vif(predictors):
vif = pd.DataFrame()
vif["feature"] = predictors.columns
# calculating VIF for each feature
vif["VIF"] = [
variance_inflation_factor(predictors.values, i)
for i in range(len(predictors.columns))
]
return vif
# checking VIF for X_train data
checking_vif(X_train)
| feature | VIF | |
|---|---|---|
| 0 | const | 3.890841e+07 |
| 1 | no_of_adults | 1.330714e+00 |
| 2 | no_of_children | 1.267522e+00 |
| 3 | no_of_weekend_nights | 1.068126e+00 |
| 4 | no_of_week_nights | 1.086490e+00 |
| 5 | type_of_meal_plan | 1.409505e+00 |
| 6 | required_car_parking_space | 1.038847e+00 |
| 7 | room_type_reserved | 1.556293e+00 |
| 8 | lead_time | 1.376522e+00 |
| 9 | arrival_year | 1.410202e+00 |
| 10 | arrival_month | 1.273765e+00 |
| 11 | arrival_date | 1.005885e+00 |
| 12 | repeated_guest | 1.781553e+00 |
| 13 | no_of_previous_cancellations | 1.394915e+00 |
| 14 | no_of_previous_bookings_not_canceled | 1.651273e+00 |
| 15 | avg_price_per_room | 1.956711e+00 |
| 16 | no_of_special_requests | 1.242152e+00 |
| 17 | market_segment_type_Complementary | 4.436203e+00 |
| 18 | market_segment_type_Corporate | 1.686666e+01 |
| 19 | market_segment_type_Offline | 6.395603e+01 |
| 20 | market_segment_type_Online | 7.107792e+01 |
The variable market_segment_type_Online exhibit high multicollinearity, Lets drop it from the dataset.
# treating market_segment_type_Online for Multicollinearity
col_to_drop = "market_segment_type_Online"
X_train = X_train.loc[:, ~X_train.columns.str.startswith(col_to_drop)]
X_test = X_test.loc[:, ~X_test.columns.str.startswith(col_to_drop)]
# Check VIF now
checking_vif(X_train)
| feature | VIF | |
|---|---|---|
| 0 | const | 3.882071e+07 |
| 1 | no_of_adults | 1.315329e+00 |
| 2 | no_of_children | 1.265005e+00 |
| 3 | no_of_weekend_nights | 1.067674e+00 |
| 4 | no_of_week_nights | 1.085828e+00 |
| 5 | type_of_meal_plan | 1.406939e+00 |
| 6 | required_car_parking_space | 1.038717e+00 |
| 7 | room_type_reserved | 1.550773e+00 |
| 8 | lead_time | 1.371706e+00 |
| 9 | arrival_year | 1.407378e+00 |
| 10 | arrival_month | 1.272646e+00 |
| 11 | arrival_date | 1.005871e+00 |
| 12 | repeated_guest | 1.778199e+00 |
| 13 | no_of_previous_cancellations | 1.394672e+00 |
| 14 | no_of_previous_bookings_not_canceled | 1.651016e+00 |
| 15 | avg_price_per_room | 1.955671e+00 |
| 16 | no_of_special_requests | 1.237534e+00 |
| 17 | market_segment_type_Complementary | 1.292321e+00 |
| 18 | market_segment_type_Corporate | 1.490731e+00 |
| 19 | market_segment_type_Offline | 1.561369e+00 |
We will do the following repeatedly using a loop:
# initial list of columns
cols = X_train.columns.tolist()
# setting an initial max p-value
max_p_value = 1
while len(cols) > 0:
# defining the train set
X_train_aux = X_train[cols]
# fitting the model
model = sm.Logit(y_train, X_train_aux).fit(disp=False)
# getting the p-values and the maximum p-value
p_values = model.pvalues
max_p_value = max(p_values)
# name of the variable with maximum p-value
feature_with_p_max = p_values.idxmax()
if max_p_value > 0.05:
cols.remove(feature_with_p_max)
else:
break
selected_features = cols
print(selected_features)
['const', 'no_of_adults', 'no_of_weekend_nights', 'no_of_week_nights', 'type_of_meal_plan', 'required_car_parking_space', 'room_type_reserved', 'lead_time', 'arrival_year', 'arrival_month', 'repeated_guest', 'no_of_previous_cancellations', 'avg_price_per_room', 'no_of_special_requests', 'market_segment_type_Corporate', 'market_segment_type_Offline']
The above columns are the significant columns. Lets create X_train2 and X_test2 with the significant columns.
# creating a new training and testing set
X_train2 = X_train[selected_features]
X_test2 = X_test[selected_features]
# building a Logistic Regression model.
hotel_booking_statsmodels_log_reg_model2 = sm.Logit(y_train,X_train2)
hotel_booking_statsmodels_log_reg_model2 = hotel_booking_statsmodels_log_reg_model2.fit(disp=False)
print(hotel_booking_statsmodels_log_reg_model2.summary())
Logit Regression Results
==============================================================================
Dep. Variable: booking_status No. Observations: 25392
Model: Logit Df Residuals: 25376
Method: MLE Df Model: 15
Date: Sat, 20 Nov 2021 Pseudo R-squ.: 0.3269
Time: 00:18:43 Log-Likelihood: -10831.
converged: True LL-Null: -16091.
Covariance Type: nonrobust LLR p-value: 0.000
=================================================================================================
coef std err z P>|z| [0.025 0.975]
-------------------------------------------------------------------------------------------------
const -888.5452 118.434 -7.502 0.000 -1120.672 -656.418
no_of_adults 0.1274 0.036 3.534 0.000 0.057 0.198
no_of_weekend_nights 0.1061 0.020 5.383 0.000 0.067 0.145
no_of_week_nights 0.0379 0.012 3.107 0.002 0.014 0.062
type_of_meal_plan -0.0923 0.041 -2.237 0.025 -0.173 -0.011
required_car_parking_space -1.6073 0.138 -11.670 0.000 -1.877 -1.337
room_type_reserved -0.1404 0.015 -9.452 0.000 -0.170 -0.111
lead_time 0.0157 0.000 59.690 0.000 0.015 0.016
arrival_year 0.4390 0.059 7.479 0.000 0.324 0.554
arrival_month -0.0425 0.006 -6.608 0.000 -0.055 -0.030
repeated_guest -2.7467 0.560 -4.908 0.000 -3.844 -1.650
no_of_previous_cancellations 0.2262 0.077 2.933 0.003 0.075 0.377
avg_price_per_room 0.0194 0.001 28.068 0.000 0.018 0.021
no_of_special_requests -1.4653 0.030 -48.980 0.000 -1.524 -1.407
market_segment_type_Corporate -0.8644 0.102 -8.495 0.000 -1.064 -0.665
market_segment_type_Offline -1.7965 0.051 -35.183 0.000 -1.897 -1.696
=================================================================================================
Now no feature has p-value greater than 0.05, so we'll consider the features in X_train2 as the significant features and hotel_booking_statsmodels_log_reg_model2 as the final model.
# converting coefficients to odds
odds = np.exp(hotel_booking_statsmodels_log_reg_model2.params)
# finding the percentage change
perc_change_odds = (np.exp(hotel_booking_statsmodels_log_reg_model2.params) - 1) * 100
# adding the odds to a dataframe
odds_df = pd.DataFrame({"Odds": odds, "Change_odd%": perc_change_odds}, index=X_train2.columns)
odds_df.sort_values(by='Odds')
| Odds | Change_odd% | |
|---|---|---|
| const | 0.000000 | -100.000000 |
| repeated_guest | 0.064140 | -93.585982 |
| market_segment_type_Offline | 0.165882 | -83.411778 |
| required_car_parking_space | 0.200433 | -79.956702 |
| no_of_special_requests | 0.231016 | -76.898385 |
| market_segment_type_Corporate | 0.421320 | -57.867979 |
| room_type_reserved | 0.869007 | -13.099329 |
| type_of_meal_plan | 0.911795 | -8.820520 |
| arrival_month | 0.958418 | -4.158230 |
| lead_time | 1.015827 | 1.582694 |
| avg_price_per_room | 1.019574 | 1.957444 |
| no_of_week_nights | 1.038642 | 3.864226 |
| no_of_weekend_nights | 1.111955 | 11.195536 |
| no_of_adults | 1.135891 | 13.589146 |
| no_of_previous_cancellations | 1.253798 | 25.379813 |
| arrival_year | 1.551085 | 55.108487 |
Interpretation for other attributes can be done similarly.
# creating confusion matrix
confusion_matrix_statsmodels(hotel_booking_statsmodels_log_reg_model2,X_train2,y_train)
# checking model performance for this model
hotel_booking_statsmodels_log_reg_model_train_perf_df = model_performance_classification_statsmodels(
hotel_booking_statsmodels_log_reg_model2,X_train2,y_train
)
print("Training performance:")
hotel_booking_statsmodels_log_reg_model_train_perf_df
Training performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.804387 | 0.630396 | 0.737549 | 0.679776 |
# ROC-AUC on training set
hotel_booking_statsmodels_log_reg_roc_auc_score = roc_auc_score(y_train,hotel_booking_statsmodels_log_reg_model2.predict(X_train2))
fpr, tpr, thresholds = roc_curve(y_train,hotel_booking_statsmodels_log_reg_model2.predict(X_train2))
plt.figure(figsize=(10,8))
plt.plot(fpr,tpr,label="Logistic Regression :Area = %0.2f)" % hotel_booking_statsmodels_log_reg_roc_auc_score)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()
# Optimal threshold as per AUC-ROC curve
# The optimal cut off would be where tpr is high and fpr is low
hotel_booking_statsmodels_optimal_idx = np.argmax(tpr - fpr) # find the point where the difference in tpr and fpr is maximum
print('optimal_idx:',hotel_booking_statsmodels_optimal_idx)
hotel_booking_statsmodels_optimal_threshold_auc_roc = thresholds[hotel_booking_statsmodels_optimal_idx]
print('optimal_threshold_auc_roc:',hotel_booking_statsmodels_optimal_threshold_auc_roc)
optimal_idx: 3907 optimal_threshold_auc_roc: 0.31697368508365786
# re-creating confusion matrix with hotel_booking_statsmodels_optimal_threshold_auc_roc
confusion_matrix_statsmodels(hotel_booking_statsmodels_log_reg_model2
,X_train2
,y_train
,threshold=hotel_booking_statsmodels_optimal_threshold_auc_roc)
# re-checking model performance for this model with hotel_booking_statsmodels_optimal_threshold_auc_roc
hotel_booking_statsmodels_log_reg_model_auc_roc_thld_train_df = model_performance_classification_statsmodels(
hotel_booking_statsmodels_log_reg_model2,
X_train2,
y_train,
threshold=hotel_booking_statsmodels_optimal_threshold_auc_roc
)
print("Training performance:")
hotel_booking_statsmodels_log_reg_model_auc_roc_thld_train_df
Training performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.775677 | 0.778788 | 0.628729 | 0.695759 |
y_pred_train2 = hotel_booking_statsmodels_log_reg_model2.predict(X_train2)
precision_array, recall_array, threshold_array = precision_recall_curve(y_train,y_pred_train2)
def plot_prec_recall_vs_tresh(precisions, recalls, thresholds):
plt.plot(thresholds, precisions[:-1], "b--", label="precision")
plt.plot(thresholds, recalls[:-1], "g--", label="recall")
plt.xlabel("Threshold")
plt.legend(loc="upper left")
plt.ylim([0, 1])
plt.figure(figsize=(10, 7))
plot_prec_recall_vs_tresh(precision_array, recall_array, threshold_array)
plt.show()
# setting the threshold
hotel_booking_statsmodels_optimal_pre_rcl_thld_curve = 0.416
# re-creating confusion matrix with hotel_booking_statsmodels_optimal_pre_rcl_thld_curve
confusion_matrix_statsmodels(hotel_booking_statsmodels_log_reg_model2
,X_train2
,y_train
,threshold=hotel_booking_statsmodels_optimal_pre_rcl_thld_curve)
# re-checking model performance for this model with hotel_booking_statsmodels_optimal_pre_rcl_thld_curve
hotel_booking_statsmodels_log_reg_model_pre_rcl_thld_train_df = model_performance_classification_statsmodels(
hotel_booking_statsmodels_log_reg_model2,
X_train2,
y_train,
threshold=hotel_booking_statsmodels_optimal_pre_rcl_thld_curve
)
print("Training performance:")
hotel_booking_statsmodels_log_reg_model_pre_rcl_thld_train_df
Training performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.799267 | 0.704293 | 0.691802 | 0.697991 |
# creating confusion matrix
confusion_matrix_statsmodels(hotel_booking_statsmodels_log_reg_model2,X_test2,y_test)
# checking model performance for this model
hotel_booking_statsmodels_log_reg_model_test_perf_df = model_performance_classification_statsmodels(
hotel_booking_statsmodels_log_reg_model2,X_test2,y_test
)
print("Testing performance:")
hotel_booking_statsmodels_log_reg_model_test_perf_df
Testing performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.80566 | 0.628052 | 0.733179 | 0.676556 |
hotel_booking_statsmodels_log_reg_roc_auc_score = roc_auc_score(y_test,hotel_booking_statsmodels_log_reg_model2.predict(X_test2))
fpr, tpr, thresholds = roc_curve(y_test,hotel_booking_statsmodels_log_reg_model2.predict(X_test2))
plt.figure(figsize=(10,8))
plt.plot(fpr,tpr,label="Logistic Regression :Area = %0.2f)" % hotel_booking_statsmodels_log_reg_roc_auc_score)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()
# re-creating confusion matrix with hotel_booking_statsmodels_optimal_threshold_auc_roc
confusion_matrix_statsmodels(hotel_booking_statsmodels_log_reg_model2
,X_test2
,y_test
,threshold=hotel_booking_statsmodels_optimal_threshold_auc_roc)
# re-checking model performance for this model with hotel_booking_statsmodels_optimal_threshold_auc_roc
hotel_booking_statsmodels_log_reg_model_auc_roc_thld_test_df = model_performance_classification_statsmodels(
hotel_booking_statsmodels_log_reg_model2,
X_test2,
y_test,
threshold=hotel_booking_statsmodels_optimal_threshold_auc_roc
)
print("testing performance:")
hotel_booking_statsmodels_log_reg_model_auc_roc_thld_test_df
testing performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.781218 | 0.785065 | 0.629984 | 0.699027 |
# re-creating confusion matrix with hotel_booking_statsmodels_optimal_pre_rcl_thld_curve
confusion_matrix_statsmodels(hotel_booking_statsmodels_log_reg_model2
,X_test2
,y_test
,threshold=hotel_booking_statsmodels_optimal_pre_rcl_thld_curve)
# re-checking model performance for this model with optimal_threshold_auc_roc
hotel_booking_statsmodels_log_reg_model_pre_rcl_thld_test_df = model_performance_classification_statsmodels(
hotel_booking_statsmodels_log_reg_model2,
X_test2,
y_test,
threshold=hotel_booking_statsmodels_optimal_pre_rcl_thld_curve
)
print("Testing performance:")
hotel_booking_statsmodels_log_reg_model_pre_rcl_thld_test_df
Testing performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.801985 | 0.706701 | 0.689283 | 0.697883 |
# training performance comparison
hotel_booking_log_reg_models_train_compare_df = pd.concat(
[
hotel_booking_sklearn_log_reg_model_train_perf_df.T,
hotel_booking_statsmodels_log_reg_model_train_perf_df.T,
hotel_booking_statsmodels_log_reg_model_auc_roc_thld_train_df.T,
hotel_booking_statsmodels_log_reg_model_pre_rcl_thld_train_df.T,
],axis=1
)
hotel_booking_log_reg_models_train_compare_df.columns=[
"Logistic Regression sklearn",
"Logistic Regression statsmodels",
"Logistic Regression 0.317 Threshold",
"Logistic Regression 0.416 Threshold"
]
print("training performance comparison:")
hotel_booking_log_reg_models_train_compare_df
training performance comparison:
| Logistic Regression sklearn | Logistic Regression statsmodels | Logistic Regression 0.317 Threshold | Logistic Regression 0.416 Threshold | |
|---|---|---|---|---|
| Accuracy | 0.803009 | 0.804387 | 0.775677 | 0.799267 |
| Recall | 0.624298 | 0.630396 | 0.778788 | 0.704293 |
| Precision | 0.737325 | 0.737549 | 0.628729 | 0.691802 |
| F1 | 0.676120 | 0.679776 | 0.695759 | 0.697991 |
# testing performance comparison
hotel_booking_log_reg_models_test_compare_df = pd.concat(
[
hotel_booking_sklearn_log_reg_model_test_perf_df.T,
hotel_booking_statsmodels_log_reg_model_test_perf_df.T,
hotel_booking_statsmodels_log_reg_model_auc_roc_thld_test_df.T,
hotel_booking_statsmodels_log_reg_model_pre_rcl_thld_test_df.T,
],axis=1
)
hotel_booking_log_reg_models_test_compare_df.columns=[
"Logistic Regression sklearn",
"Logistic Regression statsmodels",
"Logistic Regression 0.317 Threshold",
"Logistic Regression 0.416 Threshold"
]
print("Testing performance comparison:")
hotel_booking_log_reg_models_test_compare_df
Testing performance comparison:
| Logistic Regression sklearn | Logistic Regression statsmodels | Logistic Regression 0.317 Threshold | Logistic Regression 0.416 Threshold | |
|---|---|---|---|---|
| Accuracy | 0.804558 | 0.805660 | 0.781218 | 0.801985 |
| Recall | 0.624645 | 0.628052 | 0.785065 | 0.706701 |
| Precision | 0.732113 | 0.733179 | 0.629984 | 0.689283 |
| F1 | 0.674123 | 0.676556 | 0.699027 | 0.697883 |
Build a Decision Tree model (with sklearn) with the training data and then check it's performance.
# Get X and y varaibles from the copy for use in Decision Tree model
X = X_copy.copy()
y = y_copy.copy()
# Library to split data
from sklearn.model_selection import train_test_split
# splitting data for training and testing
X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.3, random_state=1)
# Libraries to build decision tree classifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
# Building the decision tress model using sklearn
Hotel_Booking_Sklearn_Tree_Model = DecisionTreeClassifier(criterion='gini',random_state=1)
Hotel_Booking_Sklearn_Tree_Model.fit(X_train,y_train)
DecisionTreeClassifier(random_state=1)
# creating confusion matrix
confusion_matrix_sklearn(Hotel_Booking_Sklearn_Tree_Model,X_train,y_train)
# checking model performance for this model
Hotel_Booking_Sklearn_Tree_Model_Train_df = model_performance_classification_sklearnmodels(Hotel_Booking_Sklearn_Tree_Model
,X_train
,y_train
)
Hotel_Booking_Sklearn_Tree_Model_Train_df
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.994211 | 0.986608 | 0.995776 | 0.991171 |
# creating confusion matrix
confusion_matrix_sklearn(Hotel_Booking_Sklearn_Tree_Model,X_test,y_test)
# checking model performance for this model
Hotel_Booking_Sklearn_Tree_Model_Test_df = model_performance_classification_sklearnmodels(Hotel_Booking_Sklearn_Tree_Model
,X_test
,y_test
)
Hotel_Booking_Sklearn_Tree_Model_Test_df
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.872921 | 0.812039 | 0.79866 | 0.805294 |
Model is giving good and generalized results on training and test set.
# Get the list of features of the Hotel booking dataset for plotting the descision tree.
feature_names = X.columns.to_list()
feature_names
['no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'type_of_meal_plan', 'required_car_parking_space', 'room_type_reserved', 'lead_time', 'arrival_year', 'arrival_month', 'arrival_date', 'repeated_guest', 'no_of_previous_cancellations', 'no_of_previous_bookings_not_canceled', 'avg_price_per_room', 'no_of_special_requests', 'market_segment_type_Complementary', 'market_segment_type_Corporate', 'market_segment_type_Offline', 'market_segment_type_Online']
# Plotting the descision tree.
plt.figure(figsize=(20,30))
tree.plot_tree(Hotel_Booking_Sklearn_Tree_Model
,feature_names=feature_names
,filled=True
,fontsize=9
,node_ids=True
,class_names=True
)
plt.show()
# Text report showing the rules of a decision tree
print(tree.export_text(Hotel_Booking_Sklearn_Tree_Model
,feature_names=feature_names
,show_weights=True
)
)
|--- lead_time <= 151.50 | |--- no_of_special_requests <= 0.50 | | |--- market_segment_type_Online <= 0.50 | | | |--- lead_time <= 90.50 | | | | |--- avg_price_per_room <= 201.50 | | | | | |--- no_of_weekend_nights <= 0.50 | | | | | | |--- market_segment_type_Offline <= 0.50 | | | | | | | |--- lead_time <= 16.50 | | | | | | | | |--- avg_price_per_room <= 68.50 | | | | | | | | | |--- repeated_guest <= 0.50 | | | | | | | | | | |--- avg_price_per_room <= 48.50 | | | | | | | | | | | |--- weights: [63.00, 0.00] class: 0 | | | | | | | | | | |--- avg_price_per_room > 48.50 | | | | | | | | | | | |--- truncated branch of depth 8 | | | | | | | | | |--- repeated_guest > 0.50 | | | | | | | | | | |--- weights: [109.00, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 68.50 | | | | | | | | | |--- arrival_date <= 29.50 | | | | | | | | | | |--- no_of_adults <= 1.50 | | | | | | | | | | | |--- truncated branch of depth 15 | | | | | | | | | | |--- no_of_adults > 1.50 | | | | | | | | | | | |--- truncated branch of depth 6 | | | | | | | | | |--- arrival_date > 29.50 | | | | | | | | | | |--- arrival_month <= 7.00 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- arrival_month > 7.00 | | | | | | | | | | | |--- weights: [0.00, 4.00] class: 1 | | | | | | | |--- lead_time > 16.50 | | | | | | | | |--- avg_price_per_room <= 135.00 | | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | | |--- lead_time <= 17.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- lead_time > 17.50 | | | | | | | | | | | |--- truncated branch of depth 12 | | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | | |--- weights: [29.00, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 135.00 | | | | | | | | | |--- weights: [0.00, 8.00] class: 1 | | | | | | |--- market_segment_type_Offline > 0.50 | | | | | | | |--- weights: [1609.00, 0.00] class: 0 | | | | | |--- no_of_weekend_nights > 0.50 | | | | | | |--- lead_time <= 68.50 | | | | | | | |--- no_of_weekend_nights <= 4.50 | | | | | | | | |--- lead_time <= 1.50 | | | | | | | | | |--- arrival_date <= 27.50 | | | | | | | | | | |--- no_of_week_nights <= 5.50 | | | | | | | | | | | |--- truncated branch of depth 7 | | | | | | | | | | |--- no_of_week_nights > 5.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | |--- arrival_date > 27.50 | | | | | | | | | | |--- arrival_month <= 2.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- arrival_month > 2.50 | | | | | | | | | | | |--- truncated branch of depth 5 | | | | | | | | |--- lead_time > 1.50 | | | | | | | | | |--- arrival_month <= 9.50 | | | | | | | | | | |--- lead_time <= 59.50 | | | | | | | | | | | |--- truncated branch of depth 17 | | | | | | | | | | |--- lead_time > 59.50 | | | | | | | | | | | |--- truncated branch of depth 5 | | | | | | | | | |--- arrival_month > 9.50 | | | | | | | | | | |--- lead_time <= 65.50 | | | | | | | | | | | |--- truncated branch of depth 12 | | | | | | | | | | |--- lead_time > 65.50 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | |--- no_of_weekend_nights > 4.50 | | | | | | | | |--- weights: [0.00, 8.00] class: 1 | | | | | | |--- lead_time > 68.50 | | | | | | | |--- avg_price_per_room <= 99.98 | | | | | | | | |--- arrival_month <= 3.50 | | | | | | | | | |--- avg_price_per_room <= 62.50 | | | | | | | | | | |--- weights: [21.00, 0.00] class: 0 | | | | | | | | | |--- avg_price_per_room > 62.50 | | | | | | | | | | |--- lead_time <= 77.00 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- lead_time > 77.00 | | | | | | | | | | | |--- truncated branch of depth 5 | | | | | | | | |--- arrival_month > 3.50 | | | | | | | | | |--- lead_time <= 71.50 | | | | | | | | | | |--- arrival_month <= 8.00 | | | | | | | | | | | |--- weights: [0.00, 2.00] class: 1 | | | | | | | | | | |--- arrival_month > 8.00 | | | | | | | | | | | |--- weights: [3.00, 0.00] class: 0 | | | | | | | | | |--- lead_time > 71.50 | | | | | | | | | | |--- no_of_week_nights <= 2.50 | | | | | | | | | | | |--- truncated branch of depth 5 | | | | | | | | | | |--- no_of_week_nights > 2.50 | | | | | | | | | | | |--- truncated branch of depth 6 | | | | | | | |--- avg_price_per_room > 99.98 | | | | | | | | |--- no_of_adults <= 1.50 | | | | | | | | | |--- avg_price_per_room <= 123.25 | | | | | | | | | | |--- weights: [0.00, 52.00] class: 1 | | | | | | | | | |--- avg_price_per_room > 123.25 | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | |--- no_of_adults > 1.50 | | | | | | | | | |--- arrival_date <= 23.50 | | | | | | | | | | |--- arrival_date <= 3.00 | | | | | | | | | | | |--- weights: [0.00, 2.00] class: 1 | | | | | | | | | | |--- arrival_date > 3.00 | | | | | | | | | | | |--- truncated branch of depth 6 | | | | | | | | | |--- arrival_date > 23.50 | | | | | | | | | | |--- avg_price_per_room <= 131.67 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- avg_price_per_room > 131.67 | | | | | | | | | | | |--- weights: [2.00, 0.00] class: 0 | | | | |--- avg_price_per_room > 201.50 | | | | | |--- arrival_month <= 10.50 | | | | | | |--- weights: [0.00, 17.00] class: 1 | | | | | |--- arrival_month > 10.50 | | | | | | |--- weights: [2.00, 0.00] class: 0 | | | |--- lead_time > 90.50 | | | | |--- lead_time <= 117.50 | | | | | |--- avg_price_per_room <= 93.58 | | | | | | |--- arrival_date <= 6.50 | | | | | | | |--- no_of_week_nights <= 2.50 | | | | | | | | |--- no_of_weekend_nights <= 1.50 | | | | | | | | | |--- avg_price_per_room <= 80.38 | | | | | | | | | | |--- arrival_month <= 3.00 | | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | | | |--- arrival_month > 3.00 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | |--- avg_price_per_room > 80.38 | | | | | | | | | | |--- lead_time <= 93.00 | | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | | | |--- lead_time > 93.00 | | | | | | | | | | | |--- weights: [3.00, 0.00] class: 0 | | | | | | | | |--- no_of_weekend_nights > 1.50 | | | | | | | | | |--- no_of_adults <= 2.50 | | | | | | | | | | |--- weights: [5.00, 0.00] class: 0 | | | | | | | | | |--- no_of_adults > 2.50 | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | |--- no_of_week_nights > 2.50 | | | | | | | | |--- arrival_date <= 5.50 | | | | | | | | | |--- weights: [35.00, 0.00] class: 0 | | | | | | | | |--- arrival_date > 5.50 | | | | | | | | | |--- arrival_month <= 9.00 | | | | | | | | | | |--- weights: [3.00, 0.00] class: 0 | | | | | | | | | |--- arrival_month > 9.00 | | | | | | | | | | |--- lead_time <= 95.50 | | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | | | |--- lead_time > 95.50 | | | | | | | | | | | |--- weights: [0.00, 2.00] class: 1 | | | | | | |--- arrival_date > 6.50 | | | | | | | |--- avg_price_per_room <= 66.50 | | | | | | | | |--- no_of_weekend_nights <= 1.50 | | | | | | | | | |--- type_of_meal_plan <= 0.50 | | | | | | | | | | |--- no_of_weekend_nights <= 0.50 | | | | | | | | | | | |--- weights: [2.00, 0.00] class: 0 | | | | | | | | | | |--- no_of_weekend_nights > 0.50 | | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | | |--- type_of_meal_plan > 0.50 | | | | | | | | | | |--- weights: [24.00, 0.00] class: 0 | | | | | | | | |--- no_of_weekend_nights > 1.50 | | | | | | | | | |--- avg_price_per_room <= 58.75 | | | | | | | | | | |--- weights: [6.00, 0.00] class: 0 | | | | | | | | | |--- avg_price_per_room > 58.75 | | | | | | | | | | |--- lead_time <= 97.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- lead_time > 97.50 | | | | | | | | | | | |--- weights: [0.00, 39.00] class: 1 | | | | | | | |--- avg_price_per_room > 66.50 | | | | | | | | |--- type_of_meal_plan <= 1.50 | | | | | | | | | |--- arrival_date <= 29.50 | | | | | | | | | | |--- no_of_week_nights <= 8.50 | | | | | | | | | | | |--- truncated branch of depth 11 | | | | | | | | | | |--- no_of_week_nights > 8.50 | | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | | |--- arrival_date > 29.50 | | | | | | | | | | |--- lead_time <= 96.00 | | | | | | | | | | | |--- weights: [8.00, 0.00] class: 0 | | | | | | | | | | |--- lead_time > 96.00 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | |--- type_of_meal_plan > 1.50 | | | | | | | | | |--- avg_price_per_room <= 82.50 | | | | | | | | | | |--- avg_price_per_room <= 76.25 | | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | | | |--- avg_price_per_room > 76.25 | | | | | | | | | | | |--- weights: [0.00, 7.00] class: 1 | | | | | | | | | |--- avg_price_per_room > 82.50 | | | | | | | | | | |--- no_of_adults <= 1.50 | | | | | | | | | | | |--- weights: [11.00, 2.00] class: 0 | | | | | | | | | | |--- no_of_adults > 1.50 | | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | |--- avg_price_per_room > 93.58 | | | | | | |--- arrival_date <= 16.50 | | | | | | | |--- arrival_month <= 7.50 | | | | | | | | |--- lead_time <= 108.50 | | | | | | | | | |--- no_of_week_nights <= 0.50 | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | | |--- no_of_week_nights > 0.50 | | | | | | | | | | |--- avg_price_per_room <= 125.00 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | | |--- avg_price_per_room > 125.00 | | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | |--- lead_time > 108.50 | | | | | | | | | |--- lead_time <= 111.50 | | | | | | | | | | |--- weights: [5.00, 0.00] class: 0 | | | | | | | | | |--- lead_time > 111.50 | | | | | | | | | | |--- lead_time <= 114.50 | | | | | | | | | | | |--- weights: [12.00, 1.00] class: 0 | | | | | | | | | | |--- lead_time > 114.50 | | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | |--- arrival_month > 7.50 | | | | | | | | |--- avg_price_per_room <= 108.50 | | | | | | | | | |--- no_of_weekend_nights <= 0.50 | | | | | | | | | | |--- weights: [0.00, 47.00] class: 1 | | | | | | | | | |--- no_of_weekend_nights > 0.50 | | | | | | | | | | |--- lead_time <= 113.50 | | | | | | | | | | | |--- weights: [4.00, 0.00] class: 0 | | | | | | | | | | |--- lead_time > 113.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | |--- avg_price_per_room > 108.50 | | | | | | | | | |--- no_of_weekend_nights <= 0.50 | | | | | | | | | | |--- weights: [42.00, 0.00] class: 0 | | | | | | | | | |--- no_of_weekend_nights > 0.50 | | | | | | | | | | |--- arrival_date <= 9.50 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | | |--- arrival_date > 9.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | |--- arrival_date > 16.50 | | | | | | | |--- arrival_month <= 8.50 | | | | | | | | |--- avg_price_per_room <= 127.39 | | | | | | | | | |--- no_of_adults <= 1.50 | | | | | | | | | | |--- weights: [0.00, 50.00] class: 1 | | | | | | | | | |--- no_of_adults > 1.50 | | | | | | | | | | |--- no_of_week_nights <= 2.50 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | | |--- no_of_week_nights > 2.50 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | |--- avg_price_per_room > 127.39 | | | | | | | | | |--- weights: [2.00, 0.00] class: 0 | | | | | | | |--- arrival_month > 8.50 | | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | | |--- weights: [0.00, 3.00] class: 1 | | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | | |--- avg_price_per_room <= 101.34 | | | | | | | | | | |--- weights: [0.00, 3.00] class: 1 | | | | | | | | | |--- avg_price_per_room > 101.34 | | | | | | | | | | |--- avg_price_per_room <= 177.83 | | | | | | | | | | | |--- weights: [8.00, 0.00] class: 0 | | | | | | | | | | |--- avg_price_per_room > 177.83 | | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | |--- lead_time > 117.50 | | | | | |--- no_of_week_nights <= 1.50 | | | | | | |--- arrival_date <= 7.50 | | | | | | | |--- weights: [51.00, 0.00] class: 0 | | | | | | |--- arrival_date > 7.50 | | | | | | | |--- avg_price_per_room <= 93.58 | | | | | | | | |--- avg_price_per_room <= 65.38 | | | | | | | | | |--- weights: [0.00, 3.00] class: 1 | | | | | | | | |--- avg_price_per_room > 65.38 | | | | | | | | | |--- avg_price_per_room <= 89.88 | | | | | | | | | | |--- weights: [24.00, 0.00] class: 0 | | | | | | | | | |--- avg_price_per_room > 89.88 | | | | | | | | | | |--- avg_price_per_room <= 91.33 | | | | | | | | | | | |--- weights: [8.00, 2.00] class: 0 | | | | | | | | | | |--- avg_price_per_room > 91.33 | | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | |--- avg_price_per_room > 93.58 | | | | | | | | |--- arrival_date <= 28.00 | | | | | | | | | |--- type_of_meal_plan <= 1.50 | | | | | | | | | | |--- no_of_adults <= 2.50 | | | | | | | | | | | |--- weights: [0.00, 17.00] class: 1 | | | | | | | | | | |--- no_of_adults > 2.50 | | | | | | | | | | | |--- weights: [1.00, 1.00] class: 0 | | | | | | | | | |--- type_of_meal_plan > 1.50 | | | | | | | | | | |--- avg_price_per_room <= 118.38 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- avg_price_per_room > 118.38 | | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | |--- arrival_date > 28.00 | | | | | | | | | |--- weights: [13.00, 1.00] class: 0 | | | | | |--- no_of_week_nights > 1.50 | | | | | | |--- no_of_adults <= 1.50 | | | | | | | |--- weights: [113.00, 0.00] class: 0 | | | | | | |--- no_of_adults > 1.50 | | | | | | | |--- lead_time <= 125.50 | | | | | | | | |--- avg_price_per_room <= 90.85 | | | | | | | | | |--- avg_price_per_room <= 87.50 | | | | | | | | | | |--- no_of_week_nights <= 3.50 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | | |--- no_of_week_nights > 3.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | |--- avg_price_per_room > 87.50 | | | | | | | | | | |--- weights: [0.00, 10.00] class: 1 | | | | | | | | |--- avg_price_per_room > 90.85 | | | | | | | | | |--- weights: [14.00, 0.00] class: 0 | | | | | | | |--- lead_time > 125.50 | | | | | | | | |--- avg_price_per_room <= 216.00 | | | | | | | | | |--- arrival_date <= 19.50 | | | | | | | | | | |--- arrival_date <= 10.50 | | | | | | | | | | | |--- truncated branch of depth 5 | | | | | | | | | | |--- arrival_date > 10.50 | | | | | | | | | | | |--- truncated branch of depth 9 | | | | | | | | | |--- arrival_date > 19.50 | | | | | | | | | | |--- lead_time <= 128.00 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- lead_time > 128.00 | | | | | | | | | | | |--- weights: [75.00, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 216.00 | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | |--- market_segment_type_Online > 0.50 | | | |--- lead_time <= 13.50 | | | | |--- avg_price_per_room <= 202.67 | | | | | |--- lead_time <= 3.50 | | | | | | |--- arrival_month <= 5.50 | | | | | | | |--- no_of_weekend_nights <= 1.50 | | | | | | | | |--- arrival_month <= 1.50 | | | | | | | | | |--- weights: [56.00, 0.00] class: 0 | | | | | | | | |--- arrival_month > 1.50 | | | | | | | | | |--- avg_price_per_room <= 77.50 | | | | | | | | | | |--- weights: [24.00, 0.00] class: 0 | | | | | | | | | |--- avg_price_per_room > 77.50 | | | | | | | | | | |--- arrival_date <= 26.50 | | | | | | | | | | | |--- truncated branch of depth 14 | | | | | | | | | | |--- arrival_date > 26.50 | | | | | | | | | | | |--- truncated branch of depth 5 | | | | | | | |--- no_of_weekend_nights > 1.50 | | | | | | | | |--- arrival_date <= 25.50 | | | | | | | | | |--- avg_price_per_room <= 134.22 | | | | | | | | | | |--- lead_time <= 2.50 | | | | | | | | | | | |--- weights: [17.00, 0.00] class: 0 | | | | | | | | | | |--- lead_time > 2.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | |--- avg_price_per_room > 134.22 | | | | | | | | | | |--- weights: [0.00, 2.00] class: 1 | | | | | | | | |--- arrival_date > 25.50 | | | | | | | | | |--- arrival_month <= 1.50 | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | | |--- arrival_month > 1.50 | | | | | | | | | | |--- weights: [0.00, 14.00] class: 1 | | | | | | |--- arrival_month > 5.50 | | | | | | | |--- no_of_week_nights <= 8.50 | | | | | | | | |--- arrival_month <= 9.50 | | | | | | | | | |--- avg_price_per_room <= 76.35 | | | | | | | | | | |--- avg_price_per_room <= 74.40 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | | |--- avg_price_per_room > 74.40 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | |--- avg_price_per_room > 76.35 | | | | | | | | | | |--- avg_price_per_room <= 118.04 | | | | | | | | | | | |--- truncated branch of depth 8 | | | | | | | | | | |--- avg_price_per_room > 118.04 | | | | | | | | | | | |--- truncated branch of depth 16 | | | | | | | | |--- arrival_month > 9.50 | | | | | | | | | |--- avg_price_per_room <= 178.00 | | | | | | | | | | |--- lead_time <= 1.50 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | | |--- lead_time > 1.50 | | | | | | | | | | | |--- truncated branch of depth 5 | | | | | | | | | |--- avg_price_per_room > 178.00 | | | | | | | | | | |--- lead_time <= 0.50 | | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | | | |--- lead_time > 0.50 | | | | | | | | | | | |--- weights: [2.00, 0.00] class: 0 | | | | | | | |--- no_of_week_nights > 8.50 | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | |--- lead_time > 3.50 | | | | | | |--- avg_price_per_room <= 99.38 | | | | | | | |--- avg_price_per_room <= 78.90 | | | | | | | | |--- no_of_week_nights <= 11.00 | | | | | | | | | |--- no_of_week_nights <= 5.50 | | | | | | | | | | |--- arrival_date <= 23.50 | | | | | | | | | | | |--- weights: [100.00, 0.00] class: 0 | | | | | | | | | | |--- arrival_date > 23.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | |--- no_of_week_nights > 5.50 | | | | | | | | | | |--- type_of_meal_plan <= 0.50 | | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | | | |--- type_of_meal_plan > 0.50 | | | | | | | | | | | |--- weights: [4.00, 0.00] class: 0 | | | | | | | | |--- no_of_week_nights > 11.00 | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | |--- avg_price_per_room > 78.90 | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | |--- arrival_month <= 1.50 | | | | | | | | | | |--- weights: [23.00, 0.00] class: 0 | | | | | | | | | |--- arrival_month > 1.50 | | | | | | | | | | |--- no_of_week_nights <= 4.50 | | | | | | | | | | | |--- truncated branch of depth 11 | | | | | | | | | | |--- no_of_week_nights > 4.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | |--- weights: [42.00, 0.00] class: 0 | | | | | | |--- avg_price_per_room > 99.38 | | | | | | | |--- arrival_month <= 8.50 | | | | | | | | |--- required_car_parking_space <= 0.50 | | | | | | | | | |--- avg_price_per_room <= 119.25 | | | | | | | | | | |--- avg_price_per_room <= 117.25 | | | | | | | | | | | |--- truncated branch of depth 8 | | | | | | | | | | |--- avg_price_per_room > 117.25 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | |--- avg_price_per_room > 119.25 | | | | | | | | | | |--- no_of_weekend_nights <= 1.50 | | | | | | | | | | | |--- truncated branch of depth 12 | | | | | | | | | | |--- no_of_weekend_nights > 1.50 | | | | | | | | | | | |--- truncated branch of depth 6 | | | | | | | | |--- required_car_parking_space > 0.50 | | | | | | | | | |--- weights: [5.00, 0.00] class: 0 | | | | | | | |--- arrival_month > 8.50 | | | | | | | | |--- lead_time <= 9.50 | | | | | | | | | |--- arrival_date <= 6.50 | | | | | | | | | | |--- type_of_meal_plan <= 0.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- type_of_meal_plan > 0.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | |--- arrival_date > 6.50 | | | | | | | | | | |--- arrival_month <= 9.50 | | | | | | | | | | | |--- truncated branch of depth 5 | | | | | | | | | | |--- arrival_month > 9.50 | | | | | | | | | | | |--- weights: [34.00, 0.00] class: 0 | | | | | | | | |--- lead_time > 9.50 | | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | | |--- arrival_date <= 26.00 | | | | | | | | | | | |--- truncated branch of depth 8 | | | | | | | | | | |--- arrival_date > 26.00 | | | | | | | | | | | |--- weights: [5.00, 0.00] class: 0 | | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | | |--- weights: [10.00, 0.00] class: 0 | | | | |--- avg_price_per_room > 202.67 | | | | | |--- arrival_month <= 11.50 | | | | | | |--- weights: [0.00, 32.00] class: 1 | | | | | |--- arrival_month > 11.50 | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | |--- lead_time > 13.50 | | | | |--- avg_price_per_room <= 105.27 | | | | | |--- avg_price_per_room <= 60.07 | | | | | | |--- lead_time <= 84.50 | | | | | | | |--- lead_time <= 51.50 | | | | | | | | |--- lead_time <= 50.50 | | | | | | | | | |--- avg_price_per_room <= 21.67 | | | | | | | | | | |--- weights: [19.00, 0.00] class: 0 | | | | | | | | | |--- avg_price_per_room > 21.67 | | | | | | | | | | |--- avg_price_per_room <= 49.84 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- avg_price_per_room > 49.84 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | |--- lead_time > 50.50 | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | |--- lead_time > 51.50 | | | | | | | | |--- weights: [32.00, 0.00] class: 0 | | | | | | |--- lead_time > 84.50 | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | |--- arrival_date <= 19.00 | | | | | | | | | |--- lead_time <= 139.00 | | | | | | | | | | |--- weights: [0.00, 8.00] class: 1 | | | | | | | | | |--- lead_time > 139.00 | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | |--- arrival_date > 19.00 | | | | | | | | | |--- lead_time <= 87.50 | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | | |--- lead_time > 87.50 | | | | | | | | | | |--- no_of_weekend_nights <= 0.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- no_of_weekend_nights > 0.50 | | | | | | | | | | | |--- weights: [6.00, 0.00] class: 0 | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | |--- avg_price_per_room <= 59.43 | | | | | | | | | |--- weights: [14.00, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 59.43 | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | |--- avg_price_per_room > 60.07 | | | | | | |--- lead_time <= 25.50 | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | |--- arrival_month <= 1.50 | | | | | | | | | |--- weights: [29.00, 0.00] class: 0 | | | | | | | | |--- arrival_month > 1.50 | | | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | | | |--- lead_time <= 14.50 | | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | | | |--- lead_time > 14.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | | | |--- no_of_week_nights <= 3.50 | | | | | | | | | | | |--- truncated branch of depth 14 | | | | | | | | | | |--- no_of_week_nights > 3.50 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | |--- arrival_month > 11.50 | | | | | | | | |--- weights: [54.00, 0.00] class: 0 | | | | | | |--- lead_time > 25.50 | | | | | | | |--- type_of_meal_plan <= 0.50 | | | | | | | | |--- required_car_parking_space <= 0.50 | | | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | | | |--- arrival_month <= 8.50 | | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | | | |--- arrival_month > 8.50 | | | | | | | | | | | |--- weights: [6.00, 0.00] class: 0 | | | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | | | |--- no_of_adults <= 1.50 | | | | | | | | | | | |--- truncated branch of depth 5 | | | | | | | | | | |--- no_of_adults > 1.50 | | | | | | | | | | | |--- truncated branch of depth 15 | | | | | | | | |--- required_car_parking_space > 0.50 | | | | | | | | | |--- weights: [6.00, 0.00] class: 0 | | | | | | | |--- type_of_meal_plan > 0.50 | | | | | | | | |--- type_of_meal_plan <= 1.50 | | | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | | | |--- lead_time <= 60.50 | | | | | | | | | | | |--- truncated branch of depth 6 | | | | | | | | | | |--- lead_time > 60.50 | | | | | | | | | | | |--- truncated branch of depth 10 | | | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | | | |--- required_car_parking_space <= 0.50 | | | | | | | | | | | |--- truncated branch of depth 28 | | | | | | | | | | |--- required_car_parking_space > 0.50 | | | | | | | | | | | |--- weights: [12.00, 0.00] class: 0 | | | | | | | | |--- type_of_meal_plan > 1.50 | | | | | | | | | |--- arrival_month <= 5.00 | | | | | | | | | | |--- weights: [2.00, 0.00] class: 0 | | | | | | | | | |--- arrival_month > 5.00 | | | | | | | | | | |--- lead_time <= 57.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- lead_time > 57.50 | | | | | | | | | | | |--- weights: [0.00, 35.00] class: 1 | | | | |--- avg_price_per_room > 105.27 | | | | | |--- required_car_parking_space <= 0.50 | | | | | | |--- arrival_month <= 10.50 | | | | | | | |--- avg_price_per_room <= 195.30 | | | | | | | | |--- lead_time <= 54.50 | | | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | | | |--- arrival_month <= 9.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- arrival_month > 9.50 | | | | | | | | | | | |--- weights: [11.00, 0.00] class: 0 | | | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | | | |--- lead_time <= 33.50 | | | | | | | | | | | |--- truncated branch of depth 16 | | | | | | | | | | |--- lead_time > 33.50 | | | | | | | | | | | |--- truncated branch of depth 19 | | | | | | | | |--- lead_time > 54.50 | | | | | | | | | |--- arrival_month <= 8.50 | | | | | | | | | | |--- lead_time <= 135.50 | | | | | | | | | | | |--- truncated branch of depth 20 | | | | | | | | | | |--- lead_time > 135.50 | | | | | | | | | | | |--- truncated branch of depth 9 | | | | | | | | | |--- arrival_month > 8.50 | | | | | | | | | | |--- lead_time <= 59.50 | | | | | | | | | | | |--- truncated branch of depth 6 | | | | | | | | | | |--- lead_time > 59.50 | | | | | | | | | | | |--- truncated branch of depth 12 | | | | | | | |--- avg_price_per_room > 195.30 | | | | | | | | |--- type_of_meal_plan <= 0.50 | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | |--- type_of_meal_plan > 0.50 | | | | | | | | | |--- no_of_adults <= 1.50 | | | | | | | | | | |--- lead_time <= 59.50 | | | | | | | | | | | |--- weights: [0.00, 6.00] class: 1 | | | | | | | | | | |--- lead_time > 59.50 | | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | | |--- no_of_adults > 1.50 | | | | | | | | | | |--- weights: [0.00, 92.00] class: 1 | | | | | | |--- arrival_month > 10.50 | | | | | | | |--- lead_time <= 22.50 | | | | | | | | |--- no_of_adults <= 1.50 | | | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | | | |--- weights: [0.00, 4.00] class: 1 | | | | | | | | |--- no_of_adults > 1.50 | | | | | | | | | |--- weights: [22.00, 0.00] class: 0 | | | | | | | |--- lead_time > 22.50 | | | | | | | | |--- avg_price_per_room <= 168.06 | | | | | | | | | |--- avg_price_per_room <= 147.75 | | | | | | | | | | |--- no_of_week_nights <= 3.50 | | | | | | | | | | | |--- truncated branch of depth 8 | | | | | | | | | | |--- no_of_week_nights > 3.50 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | |--- avg_price_per_room > 147.75 | | | | | | | | | | |--- weights: [0.00, 15.00] class: 1 | | | | | | | | |--- avg_price_per_room > 168.06 | | | | | | | | | |--- arrival_date <= 2.50 | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | | |--- arrival_date > 2.50 | | | | | | | | | | |--- lead_time <= 80.00 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- lead_time > 80.00 | | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | |--- required_car_parking_space > 0.50 | | | | | | |--- no_of_weekend_nights <= 3.00 | | | | | | | |--- weights: [39.00, 0.00] class: 0 | | | | | | |--- no_of_weekend_nights > 3.00 | | | | | | | |--- weights: [0.00, 1.00] class: 1 | |--- no_of_special_requests > 0.50 | | |--- no_of_special_requests <= 1.50 | | | |--- market_segment_type_Online <= 0.50 | | | | |--- type_of_meal_plan <= 0.50 | | | | | |--- lead_time <= 63.00 | | | | | | |--- market_segment_type_Corporate <= 0.50 | | | | | | | |--- weights: [18.00, 0.00] class: 0 | | | | | | |--- market_segment_type_Corporate > 0.50 | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | |--- weights: [2.00, 1.00] class: 0 | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | |--- lead_time > 63.00 | | | | | | |--- weights: [0.00, 6.00] class: 1 | | | | |--- type_of_meal_plan > 0.50 | | | | | |--- lead_time <= 102.50 | | | | | | |--- no_of_week_nights <= 11.00 | | | | | | | |--- avg_price_per_room <= 141.50 | | | | | | | | |--- lead_time <= 91.50 | | | | | | | | | |--- avg_price_per_room <= 129.50 | | | | | | | | | | |--- weights: [856.00, 0.00] class: 0 | | | | | | | | | |--- avg_price_per_room > 129.50 | | | | | | | | | | |--- avg_price_per_room <= 131.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- avg_price_per_room > 131.50 | | | | | | | | | | | |--- weights: [15.00, 0.00] class: 0 | | | | | | | | |--- lead_time > 91.50 | | | | | | | | | |--- no_of_children <= 0.50 | | | | | | | | | | |--- weights: [43.00, 0.00] class: 0 | | | | | | | | | |--- no_of_children > 0.50 | | | | | | | | | | |--- lead_time <= 95.50 | | | | | | | | | | | |--- weights: [0.00, 2.00] class: 1 | | | | | | | | | | |--- lead_time > 95.50 | | | | | | | | | | | |--- weights: [2.00, 0.00] class: 0 | | | | | | | |--- avg_price_per_room > 141.50 | | | | | | | | |--- avg_price_per_room <= 144.00 | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | |--- avg_price_per_room > 144.00 | | | | | | | | | |--- market_segment_type_Offline <= 0.50 | | | | | | | | | | |--- no_of_weekend_nights <= 1.00 | | | | | | | | | | | |--- weights: [2.00, 0.00] class: 0 | | | | | | | | | | |--- no_of_weekend_nights > 1.00 | | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | | |--- market_segment_type_Offline > 0.50 | | | | | | | | | | |--- weights: [15.00, 0.00] class: 0 | | | | | | |--- no_of_week_nights > 11.00 | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | |--- lead_time > 102.50 | | | | | | |--- lead_time <= 104.50 | | | | | | | |--- no_of_week_nights <= 2.50 | | | | | | | | |--- arrival_month <= 4.50 | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | |--- arrival_month > 4.50 | | | | | | | | | |--- weights: [0.00, 4.00] class: 1 | | | | | | | |--- no_of_week_nights > 2.50 | | | | | | | | |--- weights: [4.00, 0.00] class: 0 | | | | | | |--- lead_time > 104.50 | | | | | | | |--- avg_price_per_room <= 141.75 | | | | | | | | |--- no_of_week_nights <= 2.50 | | | | | | | | | |--- avg_price_per_room <= 83.39 | | | | | | | | | | |--- arrival_month <= 3.50 | | | | | | | | | | | |--- weights: [6.00, 0.00] class: 0 | | | | | | | | | | |--- arrival_month > 3.50 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | |--- avg_price_per_room > 83.39 | | | | | | | | | | |--- lead_time <= 143.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- lead_time > 143.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | |--- no_of_week_nights > 2.50 | | | | | | | | | |--- avg_price_per_room <= 122.00 | | | | | | | | | | |--- weights: [54.00, 0.00] class: 0 | | | | | | | | | |--- avg_price_per_room > 122.00 | | | | | | | | | | |--- avg_price_per_room <= 131.75 | | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | | | |--- avg_price_per_room > 131.75 | | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | |--- avg_price_per_room > 141.75 | | | | | | | | |--- no_of_weekend_nights <= 1.00 | | | | | | | | | |--- weights: [0.00, 2.00] class: 1 | | | | | | | | |--- no_of_weekend_nights > 1.00 | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | |--- market_segment_type_Online > 0.50 | | | | |--- lead_time <= 8.50 | | | | | |--- lead_time <= 4.50 | | | | | | |--- no_of_week_nights <= 10.00 | | | | | | | |--- avg_price_per_room <= 219.86 | | | | | | | | |--- avg_price_per_room <= 157.64 | | | | | | | | | |--- arrival_date <= 4.50 | | | | | | | | | | |--- weights: [82.00, 0.00] class: 0 | | | | | | | | | |--- arrival_date > 4.50 | | | | | | | | | | |--- arrival_date <= 27.50 | | | | | | | | | | | |--- truncated branch of depth 11 | | | | | | | | | | |--- arrival_date > 27.50 | | | | | | | | | | | |--- weights: [69.00, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 157.64 | | | | | | | | | |--- avg_price_per_room <= 158.50 | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | | |--- avg_price_per_room > 158.50 | | | | | | | | | | |--- no_of_week_nights <= 3.50 | | | | | | | | | | | |--- truncated branch of depth 6 | | | | | | | | | | |--- no_of_week_nights > 3.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | |--- avg_price_per_room > 219.86 | | | | | | | | |--- arrival_month <= 6.00 | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | |--- arrival_month > 6.00 | | | | | | | | | |--- avg_price_per_room <= 237.25 | | | | | | | | | | |--- weights: [5.00, 0.00] class: 0 | | | | | | | | | |--- avg_price_per_room > 237.25 | | | | | | | | | | |--- arrival_date <= 11.50 | | | | | | | | | | | |--- weights: [3.00, 0.00] class: 0 | | | | | | | | | | |--- arrival_date > 11.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | |--- no_of_week_nights > 10.00 | | | | | | | |--- weights: [0.00, 2.00] class: 1 | | | | | |--- lead_time > 4.50 | | | | | | |--- arrival_date <= 13.50 | | | | | | | |--- arrival_month <= 9.50 | | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | | |--- type_of_meal_plan <= 0.50 | | | | | | | | | | |--- weights: [3.00, 0.00] class: 0 | | | | | | | | | |--- type_of_meal_plan > 0.50 | | | | | | | | | | |--- room_type_reserved <= 2.00 | | | | | | | | | | | |--- weights: [0.00, 5.00] class: 1 | | | | | | | | | | |--- room_type_reserved > 2.00 | | | | | | | | | | | |--- weights: [2.00, 0.00] class: 0 | | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | | |--- avg_price_per_room <= 185.50 | | | | | | | | | | |--- no_of_adults <= 0.50 | | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | | | |--- no_of_adults > 0.50 | | | | | | | | | | | |--- truncated branch of depth 8 | | | | | | | | | |--- avg_price_per_room > 185.50 | | | | | | | | | | |--- no_of_children <= 0.50 | | | | | | | | | | | |--- weights: [0.00, 2.00] class: 1 | | | | | | | | | | |--- no_of_children > 0.50 | | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | |--- arrival_month > 9.50 | | | | | | | | |--- avg_price_per_room <= 157.12 | | | | | | | | | |--- weights: [43.00, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 157.12 | | | | | | | | | |--- arrival_date <= 6.50 | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | | |--- arrival_date > 6.50 | | | | | | | | | | |--- weights: [2.00, 0.00] class: 0 | | | | | | |--- arrival_date > 13.50 | | | | | | | |--- type_of_meal_plan <= 0.50 | | | | | | | | |--- avg_price_per_room <= 126.33 | | | | | | | | | |--- arrival_date <= 21.50 | | | | | | | | | | |--- weights: [24.00, 0.00] class: 0 | | | | | | | | | |--- arrival_date > 21.50 | | | | | | | | | | |--- arrival_date <= 23.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- arrival_date > 23.50 | | | | | | | | | | | |--- weights: [17.00, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 126.33 | | | | | | | | | |--- arrival_month <= 8.50 | | | | | | | | | | |--- arrival_date <= 26.00 | | | | | | | | | | | |--- weights: [7.00, 0.00] class: 0 | | | | | | | | | | |--- arrival_date > 26.00 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | |--- arrival_month > 8.50 | | | | | | | | | | |--- lead_time <= 6.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- lead_time > 6.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | |--- type_of_meal_plan > 0.50 | | | | | | | | |--- avg_price_per_room <= 139.57 | | | | | | | | | |--- no_of_week_nights <= 3.50 | | | | | | | | | | |--- arrival_month <= 2.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- arrival_month > 2.50 | | | | | | | | | | | |--- weights: [97.00, 0.00] class: 0 | | | | | | | | | |--- no_of_week_nights > 3.50 | | | | | | | | | | |--- arrival_date <= 23.50 | | | | | | | | | | | |--- weights: [10.00, 0.00] class: 0 | | | | | | | | | | |--- arrival_date > 23.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | |--- avg_price_per_room > 139.57 | | | | | | | | | |--- arrival_date <= 15.50 | | | | | | | | | | |--- arrival_date <= 14.50 | | | | | | | | | | | |--- weights: [2.00, 0.00] class: 0 | | | | | | | | | | |--- arrival_date > 14.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | |--- arrival_date > 15.50 | | | | | | | | | | |--- avg_price_per_room <= 140.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- avg_price_per_room > 140.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | |--- lead_time > 8.50 | | | | | |--- required_car_parking_space <= 0.50 | | | | | | |--- avg_price_per_room <= 127.62 | | | | | | | |--- no_of_weekend_nights <= 2.50 | | | | | | | | |--- lead_time <= 43.50 | | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | | |--- arrival_month <= 1.50 | | | | | | | | | | | |--- weights: [87.00, 0.00] class: 0 | | | | | | | | | | |--- arrival_month > 1.50 | | | | | | | | | | | |--- truncated branch of depth 23 | | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | | |--- weights: [128.00, 0.00] class: 0 | | | | | | | | |--- lead_time > 43.50 | | | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | | | |--- arrival_month <= 7.50 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | | |--- arrival_month > 7.50 | | | | | | | | | | | |--- truncated branch of depth 10 | | | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | | | |--- arrival_month <= 8.50 | | | | | | | | | | | |--- truncated branch of depth 20 | | | | | | | | | | |--- arrival_month > 8.50 | | | | | | | | | | | |--- truncated branch of depth 21 | | | | | | | |--- no_of_weekend_nights > 2.50 | | | | | | | | |--- avg_price_per_room <= 119.12 | | | | | | | | | |--- arrival_month <= 1.50 | | | | | | | | | | |--- weights: [3.00, 0.00] class: 0 | | | | | | | | | |--- arrival_month > 1.50 | | | | | | | | | | |--- no_of_week_nights <= 8.50 | | | | | | | | | | | |--- truncated branch of depth 5 | | | | | | | | | | |--- no_of_week_nights > 8.50 | | | | | | | | | | | |--- weights: [0.00, 12.00] class: 1 | | | | | | | | |--- avg_price_per_room > 119.12 | | | | | | | | | |--- weights: [3.00, 0.00] class: 0 | | | | | | |--- avg_price_per_room > 127.62 | | | | | | | |--- lead_time <= 142.50 | | | | | | | | |--- arrival_month <= 8.50 | | | | | | | | | |--- arrival_date <= 19.50 | | | | | | | | | | |--- avg_price_per_room <= 177.15 | | | | | | | | | | | |--- truncated branch of depth 15 | | | | | | | | | | |--- avg_price_per_room > 177.15 | | | | | | | | | | | |--- truncated branch of depth 6 | | | | | | | | | |--- arrival_date > 19.50 | | | | | | | | | | |--- arrival_date <= 27.50 | | | | | | | | | | | |--- truncated branch of depth 11 | | | | | | | | | | |--- arrival_date > 27.50 | | | | | | | | | | | |--- truncated branch of depth 10 | | | | | | | | |--- arrival_month > 8.50 | | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | | | | |--- truncated branch of depth 7 | | | | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | | | | |--- truncated branch of depth 21 | | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | | |--- lead_time <= 100.50 | | | | | | | | | | | |--- weights: [49.00, 0.00] class: 0 | | | | | | | | | | |--- lead_time > 100.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | |--- lead_time > 142.50 | | | | | | | | |--- avg_price_per_room <= 142.65 | | | | | | | | | |--- no_of_week_nights <= 2.50 | | | | | | | | | | |--- weights: [5.00, 0.00] class: 0 | | | | | | | | | |--- no_of_week_nights > 2.50 | | | | | | | | | | |--- no_of_weekend_nights <= 0.50 | | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | | | |--- no_of_weekend_nights > 0.50 | | | | | | | | | | | |--- weights: [0.00, 2.00] class: 1 | | | | | | | | |--- avg_price_per_room > 142.65 | | | | | | | | | |--- avg_price_per_room <= 182.49 | | | | | | | | | | |--- weights: [0.00, 11.00] class: 1 | | | | | | | | | |--- avg_price_per_room > 182.49 | | | | | | | | | | |--- avg_price_per_room <= 216.45 | | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | | | |--- avg_price_per_room > 216.45 | | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | |--- required_car_parking_space > 0.50 | | | | | | |--- room_type_reserved <= 5.50 | | | | | | | |--- weights: [180.00, 0.00] class: 0 | | | | | | |--- room_type_reserved > 5.50 | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | |--- no_of_special_requests > 1.50 | | | |--- lead_time <= 90.50 | | | | |--- no_of_week_nights <= 3.50 | | | | | |--- weights: [2126.00, 0.00] class: 0 | | | | |--- no_of_week_nights > 3.50 | | | | | |--- no_of_week_nights <= 9.50 | | | | | | |--- no_of_special_requests <= 2.50 | | | | | | | |--- lead_time <= 6.50 | | | | | | | | |--- weights: [43.00, 0.00] class: 0 | | | | | | | |--- lead_time > 6.50 | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | |--- avg_price_per_room <= 93.09 | | | | | | | | | | |--- lead_time <= 54.00 | | | | | | | | | | | |--- truncated branch of depth 8 | | | | | | | | | | |--- lead_time > 54.00 | | | | | | | | | | | |--- truncated branch of depth 5 | | | | | | | | | |--- avg_price_per_room > 93.09 | | | | | | | | | | |--- avg_price_per_room <= 107.29 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- avg_price_per_room > 107.29 | | | | | | | | | | | |--- truncated branch of depth 13 | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | |--- weights: [26.00, 0.00] class: 0 | | | | | | |--- no_of_special_requests > 2.50 | | | | | | | |--- weights: [70.00, 0.00] class: 0 | | | | | |--- no_of_week_nights > 9.50 | | | | | | |--- weights: [0.00, 2.00] class: 1 | | | |--- lead_time > 90.50 | | | | |--- avg_price_per_room <= 202.95 | | | | | |--- arrival_month <= 8.50 | | | | | | |--- arrival_year <= 2017.50 | | | | | | | |--- arrival_month <= 7.50 | | | | | | | | |--- arrival_date <= 4.50 | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | |--- arrival_date > 4.50 | | | | | | | | | |--- arrival_date <= 26.00 | | | | | | | | | | |--- weights: [0.00, 5.00] class: 1 | | | | | | | | | |--- arrival_date > 26.00 | | | | | | | | | | |--- arrival_date <= 28.00 | | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | | | |--- arrival_date > 28.00 | | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | |--- arrival_month > 7.50 | | | | | | | | |--- arrival_date <= 24.50 | | | | | | | | | |--- lead_time <= 98.50 | | | | | | | | | | |--- arrival_date <= 10.50 | | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | | | |--- arrival_date > 10.50 | | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | | |--- lead_time > 98.50 | | | | | | | | | | |--- weights: [11.00, 0.00] class: 0 | | | | | | | | |--- arrival_date > 24.50 | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | |--- arrival_year > 2017.50 | | | | | | | |--- lead_time <= 150.50 | | | | | | | | |--- no_of_children <= 0.50 | | | | | | | | | |--- arrival_date <= 29.50 | | | | | | | | | | |--- avg_price_per_room <= 157.65 | | | | | | | | | | | |--- truncated branch of depth 9 | | | | | | | | | | |--- avg_price_per_room > 157.65 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | |--- arrival_date > 29.50 | | | | | | | | | | |--- no_of_week_nights <= 2.50 | | | | | | | | | | | |--- weights: [2.00, 0.00] class: 0 | | | | | | | | | | |--- no_of_week_nights > 2.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | |--- no_of_children > 0.50 | | | | | | | | | |--- arrival_month <= 4.50 | | | | | | | | | | |--- arrival_date <= 24.00 | | | | | | | | | | | |--- weights: [0.00, 2.00] class: 1 | | | | | | | | | | |--- arrival_date > 24.00 | | | | | | | | | | | |--- weights: [2.00, 0.00] class: 0 | | | | | | | | | |--- arrival_month > 4.50 | | | | | | | | | | |--- no_of_week_nights <= 4.50 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | | |--- no_of_week_nights > 4.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | |--- lead_time > 150.50 | | | | | | | | |--- weights: [0.00, 3.00] class: 1 | | | | | |--- arrival_month > 8.50 | | | | | | |--- no_of_special_requests <= 2.50 | | | | | | | |--- avg_price_per_room <= 90.42 | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | |--- lead_time <= 107.00 | | | | | | | | | | |--- room_type_reserved <= 0.50 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | | |--- room_type_reserved > 0.50 | | | | | | | | | | | |--- weights: [4.00, 0.00] class: 0 | | | | | | | | | |--- lead_time > 107.00 | | | | | | | | | | |--- arrival_date <= 17.00 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- arrival_date > 17.00 | | | | | | | | | | | |--- weights: [7.00, 0.00] class: 0 | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | |--- lead_time <= 101.00 | | | | | | | | | | |--- weights: [11.00, 0.00] class: 0 | | | | | | | | | |--- lead_time > 101.00 | | | | | | | | | | |--- arrival_date <= 7.50 | | | | | | | | | | | |--- weights: [0.00, 2.00] class: 1 | | | | | | | | | | |--- arrival_date > 7.50 | | | | | | | | | | | |--- truncated branch of depth 5 | | | | | | | |--- avg_price_per_room > 90.42 | | | | | | | | |--- no_of_adults <= 1.50 | | | | | | | | | |--- weights: [11.00, 0.00] class: 0 | | | | | | | | |--- no_of_adults > 1.50 | | | | | | | | | |--- avg_price_per_room <= 153.15 | | | | | | | | | | |--- no_of_weekend_nights <= 1.50 | | | | | | | | | | | |--- truncated branch of depth 8 | | | | | | | | | | |--- no_of_weekend_nights > 1.50 | | | | | | | | | | | |--- truncated branch of depth 6 | | | | | | | | | |--- avg_price_per_room > 153.15 | | | | | | | | | | |--- lead_time <= 100.00 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- lead_time > 100.00 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | |--- no_of_special_requests > 2.50 | | | | | | | |--- weights: [52.00, 0.00] class: 0 | | | | |--- avg_price_per_room > 202.95 | | | | | |--- weights: [0.00, 7.00] class: 1 |--- lead_time > 151.50 | |--- avg_price_per_room <= 100.04 | | |--- no_of_special_requests <= 0.50 | | | |--- market_segment_type_Online <= 0.50 | | | | |--- no_of_adults <= 1.50 | | | | | |--- lead_time <= 163.50 | | | | | | |--- lead_time <= 162.50 | | | | | | | |--- no_of_week_nights <= 2.00 | | | | | | | | |--- weights: [4.00, 0.00] class: 0 | | | | | | | |--- no_of_week_nights > 2.00 | | | | | | | | |--- weights: [1.00, 1.00] class: 0 | | | | | | |--- lead_time > 162.50 | | | | | | | |--- weights: [0.00, 15.00] class: 1 | | | | | |--- lead_time > 163.50 | | | | | | |--- lead_time <= 341.00 | | | | | | | |--- lead_time <= 173.00 | | | | | | | | |--- arrival_date <= 3.50 | | | | | | | | | |--- arrival_month <= 9.50 | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | | |--- arrival_month > 9.50 | | | | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | | | | |--- weights: [61.00, 6.00] class: 0 | | | | | | | | |--- arrival_date > 3.50 | | | | | | | | | |--- avg_price_per_room <= 70.85 | | | | | | | | | | |--- weights: [3.00, 0.00] class: 0 | | | | | | | | | |--- avg_price_per_room > 70.85 | | | | | | | | | | |--- weights: [0.00, 9.00] class: 1 | | | | | | | |--- lead_time > 173.00 | | | | | | | | |--- arrival_month <= 5.50 | | | | | | | | | |--- arrival_date <= 7.50 | | | | | | | | | | |--- weights: [0.00, 3.00] class: 1 | | | | | | | | | |--- arrival_date > 7.50 | | | | | | | | | | |--- weights: [9.00, 0.00] class: 0 | | | | | | | | |--- arrival_month > 5.50 | | | | | | | | | |--- avg_price_per_room <= 98.00 | | | | | | | | | | |--- avg_price_per_room <= 55.21 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- avg_price_per_room > 55.21 | | | | | | | | | | | |--- truncated branch of depth 6 | | | | | | | | | |--- avg_price_per_room > 98.00 | | | | | | | | | | |--- lead_time <= 231.50 | | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | | | |--- lead_time > 231.50 | | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | |--- lead_time > 341.00 | | | | | | | |--- no_of_week_nights <= 4.00 | | | | | | | | |--- lead_time <= 402.00 | | | | | | | | | |--- avg_price_per_room <= 80.00 | | | | | | | | | | |--- weights: [5.00, 0.00] class: 0 | | | | | | | | | |--- avg_price_per_room > 80.00 | | | | | | | | | | |--- no_of_weekend_nights <= 1.00 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- no_of_weekend_nights > 1.00 | | | | | | | | | | | |--- weights: [3.00, 2.00] class: 0 | | | | | | | | |--- lead_time > 402.00 | | | | | | | | | |--- weights: [0.00, 3.00] class: 1 | | | | | | | |--- no_of_week_nights > 4.00 | | | | | | | | |--- avg_price_per_room <= 88.33 | | | | | | | | | |--- weights: [0.00, 7.00] class: 1 | | | | | | | | |--- avg_price_per_room > 88.33 | | | | | | | | | |--- weights: [1.00, 1.00] class: 0 | | | | |--- no_of_adults > 1.50 | | | | | |--- avg_price_per_room <= 84.58 | | | | | | |--- lead_time <= 244.00 | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | |--- no_of_weekend_nights <= 1.50 | | | | | | | | | |--- arrival_date <= 19.00 | | | | | | | | | | |--- lead_time <= 166.50 | | | | | | | | | | | |--- weights: [2.00, 0.00] class: 0 | | | | | | | | | | |--- lead_time > 166.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | |--- arrival_date > 19.00 | | | | | | | | | | |--- weights: [3.00, 0.00] class: 0 | | | | | | | | |--- no_of_weekend_nights > 1.50 | | | | | | | | | |--- weights: [24.00, 0.00] class: 0 | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | |--- avg_price_per_room <= 66.50 | | | | | | | | | |--- no_of_weekend_nights <= 0.50 | | | | | | | | | | |--- arrival_date <= 16.00 | | | | | | | | | | | |--- weights: [0.00, 7.00] class: 1 | | | | | | | | | | |--- arrival_date > 16.00 | | | | | | | | | | | |--- weights: [2.00, 0.00] class: 0 | | | | | | | | | |--- no_of_weekend_nights > 0.50 | | | | | | | | | | |--- avg_price_per_room <= 27.77 | | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | | | |--- avg_price_per_room > 27.77 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | |--- avg_price_per_room > 66.50 | | | | | | | | | |--- type_of_meal_plan <= 1.50 | | | | | | | | | | |--- avg_price_per_room <= 75.75 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | | |--- avg_price_per_room > 75.75 | | | | | | | | | | | |--- truncated branch of depth 6 | | | | | | | | | |--- type_of_meal_plan > 1.50 | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | |--- lead_time > 244.00 | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | | |--- weights: [34.00, 0.00] class: 0 | | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | | |--- avg_price_per_room <= 80.38 | | | | | | | | | | |--- no_of_week_nights <= 3.50 | | | | | | | | | | | |--- truncated branch of depth 6 | | | | | | | | | | |--- no_of_week_nights > 3.50 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | |--- avg_price_per_room > 80.38 | | | | | | | | | | |--- weights: [11.00, 0.00] class: 0 | | | | | | | |--- arrival_month > 11.50 | | | | | | | | |--- weights: [37.00, 0.00] class: 0 | | | | | |--- avg_price_per_room > 84.58 | | | | | | |--- arrival_month <= 11.50 | | | | | | | |--- no_of_weekend_nights <= 1.50 | | | | | | | | |--- room_type_reserved <= 1.50 | | | | | | | | | |--- arrival_date <= 2.50 | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | | |--- arrival_date > 2.50 | | | | | | | | | | |--- no_of_adults <= 2.50 | | | | | | | | | | | |--- truncated branch of depth 5 | | | | | | | | | | |--- no_of_adults > 2.50 | | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | |--- room_type_reserved > 1.50 | | | | | | | | | |--- weights: [4.00, 0.00] class: 0 | | | | | | | |--- no_of_weekend_nights > 1.50 | | | | | | | | |--- arrival_month <= 6.50 | | | | | | | | | |--- weights: [0.00, 13.00] class: 1 | | | | | | | | |--- arrival_month > 6.50 | | | | | | | | | |--- weights: [14.00, 0.00] class: 0 | | | | | | |--- arrival_month > 11.50 | | | | | | | |--- weights: [9.00, 0.00] class: 0 | | | |--- market_segment_type_Online > 0.50 | | | | |--- avg_price_per_room <= 2.50 | | | | | |--- no_of_weekend_nights <= 0.50 | | | | | | |--- lead_time <= 205.00 | | | | | | | |--- weights: [2.00, 0.00] class: 0 | | | | | | |--- lead_time > 205.00 | | | | | | | |--- arrival_date <= 19.00 | | | | | | | | |--- weights: [0.00, 4.00] class: 1 | | | | | | | |--- arrival_date > 19.00 | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | |--- no_of_weekend_nights > 0.50 | | | | | | |--- weights: [9.00, 0.00] class: 0 | | | | |--- avg_price_per_room > 2.50 | | | | | |--- arrival_month <= 11.50 | | | | | | |--- weights: [0.00, 525.00] class: 1 | | | | | |--- arrival_month > 11.50 | | | | | | |--- no_of_weekend_nights <= 0.50 | | | | | | | |--- lead_time <= 263.50 | | | | | | | | |--- avg_price_per_room <= 76.87 | | | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | | | |--- weights: [0.00, 5.00] class: 1 | | | | | | | | |--- avg_price_per_room > 76.87 | | | | | | | | | |--- weights: [3.00, 0.00] class: 0 | | | | | | | |--- lead_time > 263.50 | | | | | | | | |--- weights: [0.00, 7.00] class: 1 | | | | | | |--- no_of_weekend_nights > 0.50 | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | |--- arrival_date <= 3.50 | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | |--- arrival_date > 3.50 | | | | | | | | | |--- weights: [0.00, 6.00] class: 1 | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | |--- weights: [0.00, 58.00] class: 1 | | |--- no_of_special_requests > 0.50 | | | |--- no_of_weekend_nights <= 0.50 | | | | |--- lead_time <= 180.50 | | | | | |--- lead_time <= 159.50 | | | | | | |--- arrival_month <= 8.50 | | | | | | | |--- weights: [8.00, 0.00] class: 0 | | | | | | |--- arrival_month > 8.50 | | | | | | | |--- arrival_date <= 23.50 | | | | | | | | |--- lead_time <= 156.50 | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | |--- lead_time > 156.50 | | | | | | | | | |--- weights: [2.00, 0.00] class: 0 | | | | | | | |--- arrival_date > 23.50 | | | | | | | | |--- weights: [0.00, 4.00] class: 1 | | | | | |--- lead_time > 159.50 | | | | | | |--- no_of_adults <= 0.50 | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | |--- no_of_adults > 0.50 | | | | | | | |--- arrival_date <= 1.50 | | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | | |--- weights: [0.00, 2.00] class: 1 | | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | | |--- weights: [2.00, 0.00] class: 0 | | | | | | | |--- arrival_date > 1.50 | | | | | | | | |--- weights: [48.00, 0.00] class: 0 | | | | |--- lead_time > 180.50 | | | | | |--- market_segment_type_Online <= 0.50 | | | | | | |--- avg_price_per_room <= 96.37 | | | | | | | |--- no_of_adults <= 1.50 | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | |--- no_of_adults > 1.50 | | | | | | | | |--- lead_time <= 302.50 | | | | | | | | | |--- weights: [15.00, 0.00] class: 0 | | | | | | | | |--- lead_time > 302.50 | | | | | | | | | |--- weights: [2.00, 1.00] class: 0 | | | | | | |--- avg_price_per_room > 96.37 | | | | | | | |--- weights: [0.00, 2.00] class: 1 | | | | | |--- market_segment_type_Online > 0.50 | | | | | | |--- no_of_special_requests <= 2.50 | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | |--- no_of_week_nights <= 0.50 | | | | | | | | | |--- weights: [2.00, 0.00] class: 0 | | | | | | | | |--- no_of_week_nights > 0.50 | | | | | | | | | |--- weights: [0.00, 125.00] class: 1 | | | | | | | |--- arrival_month > 11.50 | | | | | | | | |--- lead_time <= 300.50 | | | | | | | | | |--- lead_time <= 226.50 | | | | | | | | | | |--- weights: [0.00, 3.00] class: 1 | | | | | | | | | |--- lead_time > 226.50 | | | | | | | | | | |--- lead_time <= 272.00 | | | | | | | | | | | |--- weights: [6.00, 0.00] class: 0 | | | | | | | | | | |--- lead_time > 272.00 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | |--- lead_time > 300.50 | | | | | | | | | |--- weights: [0.00, 5.00] class: 1 | | | | | | |--- no_of_special_requests > 2.50 | | | | | | | |--- weights: [12.00, 0.00] class: 0 | | | |--- no_of_weekend_nights > 0.50 | | | | |--- market_segment_type_Offline <= 0.50 | | | | | |--- no_of_week_nights <= 9.50 | | | | | | |--- arrival_month <= 11.50 | | | | | | | |--- arrival_date <= 27.50 | | | | | | | | |--- avg_price_per_room <= 81.12 | | | | | | | | | |--- lead_time <= 153.50 | | | | | | | | | | |--- arrival_date <= 4.50 | | | | | | | | | | | |--- weights: [0.00, 2.00] class: 1 | | | | | | | | | | |--- arrival_date > 4.50 | | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | | |--- lead_time > 153.50 | | | | | | | | | | |--- lead_time <= 157.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- lead_time > 157.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | |--- avg_price_per_room > 81.12 | | | | | | | | | |--- no_of_week_nights <= 6.50 | | | | | | | | | | |--- lead_time <= 233.00 | | | | | | | | | | | |--- truncated branch of depth 13 | | | | | | | | | | |--- lead_time > 233.00 | | | | | | | | | | | |--- truncated branch of depth 10 | | | | | | | | | |--- no_of_week_nights > 6.50 | | | | | | | | | | |--- lead_time <= 204.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- lead_time > 204.50 | | | | | | | | | | | |--- weights: [0.00, 3.00] class: 1 | | | | | | | |--- arrival_date > 27.50 | | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | | |--- lead_time <= 224.50 | | | | | | | | | | |--- lead_time <= 175.50 | | | | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | | | | |--- lead_time > 175.50 | | | | | | | | | | | |--- weights: [0.00, 10.00] class: 1 | | | | | | | | | |--- lead_time > 224.50 | | | | | | | | | | |--- weights: [4.00, 0.00] class: 0 | | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | | |--- lead_time <= 269.00 | | | | | | | | | | |--- lead_time <= 176.00 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | | |--- lead_time > 176.00 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | |--- lead_time > 269.00 | | | | | | | | | | |--- weights: [0.00, 3.00] class: 1 | | | | | | |--- arrival_month > 11.50 | | | | | | | |--- arrival_date <= 14.50 | | | | | | | | |--- arrival_date <= 3.00 | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | |--- arrival_date > 3.00 | | | | | | | | | |--- lead_time <= 217.50 | | | | | | | | | | |--- weights: [8.00, 0.00] class: 0 | | | | | | | | | |--- lead_time > 217.50 | | | | | | | | | | |--- type_of_meal_plan <= 0.50 | | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | | | |--- type_of_meal_plan > 0.50 | | | | | | | | | | | |--- weights: [3.00, 0.00] class: 0 | | | | | | | |--- arrival_date > 14.50 | | | | | | | | |--- no_of_week_nights <= 2.50 | | | | | | | | | |--- no_of_special_requests <= 1.50 | | | | | | | | | | |--- weights: [0.00, 8.00] class: 1 | | | | | | | | | |--- no_of_special_requests > 1.50 | | | | | | | | | | |--- arrival_date <= 19.50 | | | | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | | | | | |--- arrival_date > 19.50 | | | | | | | | | | | |--- weights: [2.00, 0.00] class: 0 | | | | | | | | |--- no_of_week_nights > 2.50 | | | | | | | | | |--- no_of_week_nights <= 4.50 | | | | | | | | | | |--- no_of_special_requests <= 1.50 | | | | | | | | | | | |--- weights: [8.00, 0.00] class: 0 | | | | | | | | | | |--- no_of_special_requests > 1.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | |--- no_of_week_nights > 4.50 | | | | | | | | | | |--- no_of_special_requests <= 2.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- no_of_special_requests > 2.50 | | | | | | | | | | | |--- weights: [2.00, 0.00] class: 0 | | | | | |--- no_of_week_nights > 9.50 | | | | | | |--- no_of_special_requests <= 2.50 | | | | | | | |--- weights: [0.00, 7.00] class: 1 | | | | | | |--- no_of_special_requests > 2.50 | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | |--- market_segment_type_Offline > 0.50 | | | | | |--- lead_time <= 348.50 | | | | | | |--- no_of_week_nights <= 5.50 | | | | | | | |--- arrival_date <= 30.00 | | | | | | | | |--- weights: [137.00, 0.00] class: 0 | | | | | | | |--- arrival_date > 30.00 | | | | | | | | |--- no_of_special_requests <= 1.50 | | | | | | | | | |--- weights: [3.00, 0.00] class: 0 | | | | | | | | |--- no_of_special_requests > 1.50 | | | | | | | | | |--- weights: [2.00, 1.00] class: 0 | | | | | | |--- no_of_week_nights > 5.50 | | | | | | | |--- lead_time <= 167.00 | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | |--- lead_time > 167.00 | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | |--- lead_time > 348.50 | | | | | | |--- lead_time <= 372.50 | | | | | | | |--- avg_price_per_room <= 58.50 | | | | | | | | |--- weights: [1.00, 0.00] class: 0 | | | | | | | |--- avg_price_per_room > 58.50 | | | | | | | | |--- weights: [6.00, 2.00] class: 0 | | | | | | |--- lead_time > 372.50 | | | | | | | |--- weights: [1.00, 1.00] class: 0 | |--- avg_price_per_room > 100.04 | | |--- arrival_month <= 11.50 | | | |--- no_of_special_requests <= 2.50 | | | | |--- weights: [0.00, 2108.00] class: 1 | | | |--- no_of_special_requests > 2.50 | | | | |--- weights: [31.00, 0.00] class: 0 | | |--- arrival_month > 11.50 | | | |--- no_of_special_requests <= 0.50 | | | | |--- weights: [47.00, 0.00] class: 0 | | | |--- no_of_special_requests > 0.50 | | | | |--- arrival_date <= 24.50 | | | | | |--- weights: [5.00, 0.00] class: 0 | | | | |--- arrival_date > 24.50 | | | | | |--- lead_time <= 172.50 | | | | | | |--- arrival_date <= 28.00 | | | | | | | |--- weights: [3.00, 0.00] class: 0 | | | | | | |--- arrival_date > 28.00 | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | |--- lead_time > 172.50 | | | | | | |--- room_type_reserved <= 1.50 | | | | | | | |--- arrival_date <= 29.00 | | | | | | | | |--- weights: [2.00, 0.00] class: 0 | | | | | | | |--- arrival_date > 29.00 | | | | | | | | |--- weights: [0.00, 1.00] class: 1 | | | | | | |--- room_type_reserved > 1.50 | | | | | | | |--- weights: [0.00, 13.00] class: 1
# importance of features in the tree building ( The importance of a feature is computed as the
# (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance)
pd.DataFrame(Hotel_Booking_Sklearn_Tree_Model.feature_importances_
,columns=['Importance']
,index=X.columns
).sort_values(by='Importance'
,ascending=False
)
| Importance | |
|---|---|
| lead_time | 0.351635 |
| avg_price_per_room | 0.175306 |
| market_segment_type_Online | 0.093856 |
| arrival_date | 0.086450 |
| no_of_special_requests | 0.068312 |
| arrival_month | 0.065027 |
| no_of_week_nights | 0.042702 |
| no_of_weekend_nights | 0.039870 |
| no_of_adults | 0.026411 |
| room_type_reserved | 0.011990 |
| type_of_meal_plan | 0.011589 |
| arrival_year | 0.011514 |
| required_car_parking_space | 0.007278 |
| no_of_children | 0.003881 |
| market_segment_type_Offline | 0.002338 |
| no_of_previous_bookings_not_canceled | 0.000704 |
| market_segment_type_Corporate | 0.000619 |
| repeated_guest | 0.000307 |
| no_of_previous_cancellations | 0.000121 |
| market_segment_type_Complementary | 0.000091 |
# plot the importance of features in the tree building in a horizontal bar plot
feature_importances = Hotel_Booking_Sklearn_Tree_Model.feature_importances_
indices = np.argsort(feature_importances)
# Set the figure size and title for the plot
plt.figure(figsize=(10, 12))
plt.title("Feature Importances")
# Display a horizontal bar plot (barh) for importance of features
plt.barh(range(len(indices)), feature_importances[indices], color="pink", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()
Let's see if we can improve our model performance even more.
from sklearn.model_selection import GridSearchCV
# To get diferent metric scores
from sklearn.metrics import (
make_scorer,
)
# Choose the type of classifier.
estimator = DecisionTreeClassifier(random_state=1)
# Grid of parameters to choose from
parameters = {'max_depth': np.arange(1,15),
'min_samples_leaf': [1, 2, 5, 7, 10,15,20],
'max_leaf_nodes' : [2, 3, 5, 10],
'min_impurity_decrease': [0.001,0.01,0.1]
}
# Type of scoring used to compare parameter combinations
acc_scorer = make_scorer(recall_score)
# Run the grid search
grid_obj = GridSearchCV(estimator, parameters, scoring=acc_scorer, cv=5)
grid_obj = grid_obj.fit(X_train, y_train)
# Set the clf to the best combination of parameters
Hotel_Booking_Hyperparam_Tuning_Tree_Model = grid_obj.best_estimator_
# Fit the best algorithm to the data.
Hotel_Booking_Hyperparam_Tuning_Tree_Model.fit(X_train,y_train)
DecisionTreeClassifier(max_depth=3, max_leaf_nodes=5,
min_impurity_decrease=0.001, random_state=1)
# creating confusion matrix
confusion_matrix_sklearn(Hotel_Booking_Hyperparam_Tuning_Tree_Model,X_train,y_train)
# checking model performance for this model
Hotel_Booking_Hyperparam_Tuning_Tree_Model_Train_df = model_performance_classification_sklearnmodels(
Hotel_Booking_Hyperparam_Tuning_Tree_Model
,X_train
,y_train
)
Hotel_Booking_Hyperparam_Tuning_Tree_Model_Train_df
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.769494 | 0.763123 | 0.622391 | 0.68561 |
# creating confusion matrix
confusion_matrix_sklearn(Hotel_Booking_Hyperparam_Tuning_Tree_Model,X_test,y_test)
# checking model performance for this model
Hotel_Booking_Hyperparam_Tuning_Tree_Model_Test_df = model_performance_classification_sklearnmodels(
Hotel_Booking_Hyperparam_Tuning_Tree_Model
,X_test
,y_test
)
Hotel_Booking_Hyperparam_Tuning_Tree_Model_Test_df
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.771938 | 0.766894 | 0.619211 | 0.685185 |
plt.figure(figsize=(15,10))
tree.plot_tree(Hotel_Booking_Hyperparam_Tuning_Tree_Model
,feature_names=feature_names
,filled=True
,fontsize=9
,node_ids=True
,class_names=True
)
plt.show()
# Text report showing the rules of a decision tree
print(tree.export_text(Hotel_Booking_Hyperparam_Tuning_Tree_Model
,feature_names=feature_names
,show_weights=True
)
)
|--- lead_time <= 151.50 | |--- no_of_special_requests <= 0.50 | | |--- market_segment_type_Online <= 0.50 | | | |--- weights: [4614.00, 781.00] class: 0 | | |--- market_segment_type_Online > 0.50 | | | |--- weights: [2504.00, 2768.00] class: 1 | |--- no_of_special_requests > 0.50 | | |--- weights: [8543.00, 1200.00] class: 0 |--- lead_time > 151.50 | |--- avg_price_per_room <= 100.04 | | |--- weights: [1280.00, 1491.00] class: 1 | |--- avg_price_per_room > 100.04 | | |--- weights: [88.00, 2123.00] class: 1
# importance of features in the tree building ( The importance of a feature is computed as the
# (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance)
pd.DataFrame(Hotel_Booking_Hyperparam_Tuning_Tree_Model.feature_importances_
,columns=['Importance']
,index=X.columns
).sort_values(by='Importance'
,ascending=False
)
| Importance | |
|---|---|
| lead_time | 0.539970 |
| market_segment_type_Online | 0.214150 |
| no_of_special_requests | 0.124176 |
| avg_price_per_room | 0.121704 |
| repeated_guest | 0.000000 |
| market_segment_type_Offline | 0.000000 |
| market_segment_type_Corporate | 0.000000 |
| market_segment_type_Complementary | 0.000000 |
| no_of_previous_bookings_not_canceled | 0.000000 |
| no_of_previous_cancellations | 0.000000 |
| no_of_adults | 0.000000 |
| no_of_children | 0.000000 |
| arrival_month | 0.000000 |
| arrival_year | 0.000000 |
| room_type_reserved | 0.000000 |
| required_car_parking_space | 0.000000 |
| type_of_meal_plan | 0.000000 |
| no_of_week_nights | 0.000000 |
| no_of_weekend_nights | 0.000000 |
| arrival_date | 0.000000 |
# plot the importance of features in the tree building in a horizontal bar plot
feature_importances = Hotel_Booking_Hyperparam_Tuning_Tree_Model.feature_importances_
indices = np.argsort(feature_importances)
# Set the figure size and title for the plot
plt.figure(figsize=(8, 8))
plt.title("Feature Importances")
# Display a horizontal bar plot (barh) for importance of features
plt.barh(range(len(indices)), feature_importances[indices], color="pink", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()
# building the decison tree model
clf = DecisionTreeClassifier(random_state=1)
# Get the path using cost_complexity_pruning_path function
path = clf.cost_complexity_pruning_path(X_train,y_train)
# Get ccp_alphas and impurities
ccp_alphas, impurities = path.ccp_alphas, path.impurities
# Display the path of the tree from leaf nodes to the root. (in reverse order)
pd.DataFrame(path).round(6)
| ccp_alphas | impurities | |
|---|---|---|
| 0 | 0.000000 | 0.007572 |
| 1 | 0.000000 | 0.007573 |
| 2 | 0.000000 | 0.007573 |
| 3 | 0.000001 | 0.007574 |
| 4 | 0.000001 | 0.007575 |
| ... | ... | ... |
| 1331 | 0.006666 | 0.286897 |
| 1332 | 0.013045 | 0.299942 |
| 1333 | 0.017260 | 0.317202 |
| 1334 | 0.023990 | 0.365183 |
| 1335 | 0.076578 | 0.441761 |
1336 rows × 2 columns
Observations:
# using [:-1] to avoid the root node.
fig, ax = plt.subplots(figsize=(15, 5))
ax.plot(ccp_alphas[:-1], impurities[:-1], marker="o", drawstyle="steps-post")
ax.set_xlabel("effective alpha")
ax.set_ylabel("total impurity of leaves")
ax.set_title("Total Impurity vs effective alpha for training set")
plt.show()
Next, we train a decision tree using the effective alphas. The last value in ccp_alphas is the alpha value that prunes the whole tree, leaving the tree, clfs[-1], with one node.
# Get the combination of all possible decision trees by pruning them at different nodes and measure the impurity.
# Also print the number of nodes, ccp_alpha and depth of each possible tree.
clfs = []
i = 0
for ccp_alpha in ccp_alphas:
clf = DecisionTreeClassifier(random_state=1, ccp_alpha=ccp_alpha)
clf.fit(X_train, y_train)
clfs.append(clf)
i = i + 1
print("Number of nodes in the tree # {} is: {} with ccp_alpha: {} and depth of {}"
.format(i,clfs[-1].tree_.node_count, ccp_alpha, clfs[-1].tree_.max_depth))
Number of nodes in the tree # 1 is: 5895 with ccp_alpha: 0.0 and depth of 38 Number of nodes in the tree # 2 is: 5893 with ccp_alpha: 4.3277453485397657e-07 and depth of 38 Number of nodes in the tree # 3 is: 5891 with ccp_alpha: 4.6883907942507803e-07 and depth of 38 Number of nodes in the tree # 4 is: 5889 with ccp_alpha: 5.329960060833014e-07 and depth of 38 Number of nodes in the tree # 5 is: 5885 with ccp_alpha: 6.133546944328441e-07 and depth of 38 Number of nodes in the tree # 6 is: 5883 with ccp_alpha: 7.77285842204801e-07 and depth of 38 Number of nodes in the tree # 7 is: 5881 with ccp_alpha: 1.0939578519918804e-06 and depth of 38 Number of nodes in the tree # 8 is: 5877 with ccp_alpha: 1.2502375451335708e-06 and depth of 38 Number of nodes in the tree # 9 is: 5875 with ccp_alpha: 1.480544461342418e-06 and depth of 38 Number of nodes in the tree # 10 is: 5873 with ccp_alpha: 1.7310981394157436e-06 and depth of 38 Number of nodes in the tree # 11 is: 5871 with ccp_alpha: 1.758707360139964e-06 and depth of 38 Number of nodes in the tree # 12 is: 5867 with ccp_alpha: 1.8753563177003663e-06 and depth of 38 Number of nodes in the tree # 13 is: 5867 with ccp_alpha: 1.8753563177003663e-06 and depth of 38 Number of nodes in the tree # 14 is: 5865 with ccp_alpha: 2.250427581240434e-06 and depth of 38 Number of nodes in the tree # 15 is: 5863 with ccp_alpha: 2.5004750902671416e-06 and depth of 38 Number of nodes in the tree # 16 is: 5861 with ccp_alpha: 2.8641805579424225e-06 and depth of 38 Number of nodes in the tree # 17 is: 5857 with ccp_alpha: 3.2818735559756377e-06 and depth of 38 Number of nodes in the tree # 18 is: 5855 with ccp_alpha: 3.375641371860665e-06 and depth of 38 Number of nodes in the tree # 19 is: 5853 with ccp_alpha: 3.5006651263739847e-06 and depth of 38 Number of nodes in the tree # 20 is: 5851 with ccp_alpha: 3.58022569742794e-06 and depth of 38 Number of nodes in the tree # 21 is: 5849 with ccp_alpha: 3.89497081368541e-06 and depth of 38 Number of nodes in the tree # 22 is: 5845 with ccp_alpha: 3.9178955629477104e-06 and depth of 38 Number of nodes in the tree # 23 is: 5843 with ccp_alpha: 3.938248267170746e-06 and depth of 38 Number of nodes in the tree # 24 is: 5841 with ccp_alpha: 4.118429560440024e-06 and depth of 38 Number of nodes in the tree # 25 is: 5839 with ccp_alpha: 4.375831407967508e-06 and depth of 38 Number of nodes in the tree # 26 is: 5835 with ccp_alpha: 4.5008551624808955e-06 and depth of 38 Number of nodes in the tree # 27 is: 5835 with ccp_alpha: 4.5008551624808955e-06 and depth of 38 Number of nodes in the tree # 28 is: 5833 with ccp_alpha: 4.86871351710683e-06 and depth of 38 Number of nodes in the tree # 29 is: 5831 with ccp_alpha: 4.922810333963455e-06 and depth of 38 Number of nodes in the tree # 30 is: 5829 with ccp_alpha: 5.6260689531010855e-06 and depth of 38 Number of nodes in the tree # 31 is: 5827 with ccp_alpha: 5.907372400756157e-06 and depth of 38 Number of nodes in the tree # 32 is: 5825 with ccp_alpha: 5.931942577111016e-06 and depth of 38 Number of nodes in the tree # 33 is: 5823 with ccp_alpha: 6.001140216641126e-06 and depth of 38 Number of nodes in the tree # 34 is: 5821 with ccp_alpha: 6.329327572238662e-06 and depth of 38 Number of nodes in the tree # 35 is: 5819 with ccp_alpha: 6.364845684316407e-06 and depth of 38 Number of nodes in the tree # 36 is: 5817 with ccp_alpha: 6.5637471119512754e-06 and depth of 38 Number of nodes in the tree # 37 is: 5815 with ccp_alpha: 7.813984657084778e-06 and depth of 38 Number of nodes in the tree # 38 is: 5813 with ccp_alpha: 8.204683889939137e-06 and depth of 38 Number of nodes in the tree # 39 is: 5811 with ccp_alpha: 8.626639061421628e-06 and depth of 38 Number of nodes in the tree # 40 is: 5809 with ccp_alpha: 8.751662815935043e-06 and depth of 38 Number of nodes in the tree # 41 is: 5807 with ccp_alpha: 9.001710324961791e-06 and depth of 38 Number of nodes in the tree # 42 is: 5803 with ccp_alpha: 9.845620667926906e-06 and depth of 38 Number of nodes in the tree # 43 is: 5801 with ccp_alpha: 9.94507138174432e-06 and depth of 38 Number of nodes in the tree # 44 is: 5797 with ccp_alpha: 1.0501995379122022e-05 and depth of 38 Number of nodes in the tree # 45 is: 5797 with ccp_alpha: 1.0501995379122022e-05 and depth of 38 Number of nodes in the tree # 46 is: 5789 with ccp_alpha: 1.0501995379122035e-05 and depth of 38 Number of nodes in the tree # 47 is: 5789 with ccp_alpha: 1.0501995379122035e-05 and depth of 38 Number of nodes in the tree # 48 is: 5789 with ccp_alpha: 1.0501995379122035e-05 and depth of 38 Number of nodes in the tree # 49 is: 5789 with ccp_alpha: 1.0501995379122035e-05 and depth of 38 Number of nodes in the tree # 50 is: 5787 with ccp_alpha: 1.1372481627124168e-05 and depth of 38 Number of nodes in the tree # 51 is: 5785 with ccp_alpha: 1.1377161660715572e-05 and depth of 38 Number of nodes in the tree # 52 is: 5783 with ccp_alpha: 1.1814744801512313e-05 and depth of 38 Number of nodes in the tree # 53 is: 5777 with ccp_alpha: 1.2117686975910051e-05 and depth of 38 Number of nodes in the tree # 54 is: 5771 with ccp_alpha: 1.251691309720942e-05 and depth of 38 Number of nodes in the tree # 55 is: 5769 with ccp_alpha: 1.275242296036289e-05 and depth of 38 Number of nodes in the tree # 56 is: 5765 with ccp_alpha: 1.2852092247177187e-05 and depth of 38 Number of nodes in the tree # 57 is: 5747 with ccp_alpha: 1.3127494223902537e-05 and depth of 38 Number of nodes in the tree # 58 is: 5747 with ccp_alpha: 1.3127494223902537e-05 and depth of 38 Number of nodes in the tree # 59 is: 5747 with ccp_alpha: 1.3127494223902537e-05 and depth of 38 Number of nodes in the tree # 60 is: 5747 with ccp_alpha: 1.3127494223902537e-05 and depth of 38 Number of nodes in the tree # 61 is: 5747 with ccp_alpha: 1.3127494223902537e-05 and depth of 38 Number of nodes in the tree # 62 is: 5747 with ccp_alpha: 1.3127494223902537e-05 and depth of 38 Number of nodes in the tree # 63 is: 5747 with ccp_alpha: 1.3127494223902537e-05 and depth of 38 Number of nodes in the tree # 64 is: 5747 with ccp_alpha: 1.3127494223902537e-05 and depth of 38 Number of nodes in the tree # 65 is: 5747 with ccp_alpha: 1.3127494223902537e-05 and depth of 38 Number of nodes in the tree # 66 is: 5743 with ccp_alpha: 1.3127494223902551e-05 and depth of 38 Number of nodes in the tree # 67 is: 5743 with ccp_alpha: 1.3127494223902551e-05 and depth of 38 Number of nodes in the tree # 68 is: 5741 with ccp_alpha: 1.3502565487442605e-05 and depth of 38 Number of nodes in the tree # 69 is: 5739 with ccp_alpha: 1.3752612996469353e-05 and depth of 38 Number of nodes in the tree # 70 is: 5725 with ccp_alpha: 1.4574197668985689e-05 and depth of 38 Number of nodes in the tree # 71 is: 5721 with ccp_alpha: 1.5002850541602917e-05 and depth of 38 Number of nodes in the tree # 72 is: 5721 with ccp_alpha: 1.5002850541602917e-05 and depth of 38 Number of nodes in the tree # 73 is: 5711 with ccp_alpha: 1.565514839123777e-05 and depth of 38 Number of nodes in the tree # 74 is: 5709 with ccp_alpha: 1.575299306868304e-05 and depth of 38 Number of nodes in the tree # 75 is: 5707 with ccp_alpha: 1.6111015638425886e-05 and depth of 38 Number of nodes in the tree # 76 is: 5705 with ccp_alpha: 1.640936777987818e-05 and depth of 38 Number of nodes in the tree # 77 is: 5699 with ccp_alpha: 1.6628159350276563e-05 and depth of 38 Number of nodes in the tree # 78 is: 5697 with ccp_alpha: 1.8753563177003636e-05 and depth of 38 Number of nodes in the tree # 79 is: 5689 with ccp_alpha: 1.8835100408208e-05 and depth of 38 Number of nodes in the tree # 80 is: 5681 with ccp_alpha: 1.8933885899859445e-05 and depth of 38 Number of nodes in the tree # 81 is: 5673 with ccp_alpha: 1.9373640669146494e-05 and depth of 38 Number of nodes in the tree # 82 is: 5657 with ccp_alpha: 1.9397342211438065e-05 and depth of 38 Number of nodes in the tree # 83 is: 5649 with ccp_alpha: 1.9565732993144673e-05 and depth of 38 Number of nodes in the tree # 84 is: 5647 with ccp_alpha: 1.964009525446192e-05 and depth of 38 Number of nodes in the tree # 85 is: 5639 with ccp_alpha: 1.9691241335853813e-05 and depth of 38 Number of nodes in the tree # 86 is: 5639 with ccp_alpha: 1.9691241335853813e-05 and depth of 38 Number of nodes in the tree # 87 is: 5639 with ccp_alpha: 1.9691241335853813e-05 and depth of 38 Number of nodes in the tree # 88 is: 5639 with ccp_alpha: 1.9691241335853813e-05 and depth of 38 Number of nodes in the tree # 89 is: 5637 with ccp_alpha: 2.0168604762177498e-05 and depth of 38 Number of nodes in the tree # 90 is: 5635 with ccp_alpha: 2.0253848231163962e-05 and depth of 38 Number of nodes in the tree # 91 is: 5633 with ccp_alpha: 2.0972937780256134e-05 and depth of 38 Number of nodes in the tree # 92 is: 5629 with ccp_alpha: 2.1003990758244064e-05 and depth of 38 Number of nodes in the tree # 93 is: 5627 with ccp_alpha: 2.1879157039837567e-05 and depth of 38 Number of nodes in the tree # 94 is: 5621 with ccp_alpha: 2.187915703983757e-05 and depth of 38 Number of nodes in the tree # 95 is: 5619 with ccp_alpha: 2.1879157039837594e-05 and depth of 38 Number of nodes in the tree # 96 is: 5609 with ccp_alpha: 2.219117284457959e-05 and depth of 38 Number of nodes in the tree # 97 is: 5603 with ccp_alpha: 2.250427581240436e-05 and depth of 38 Number of nodes in the tree # 98 is: 5599 with ccp_alpha: 2.362948960302456e-05 and depth of 38 Number of nodes in the tree # 99 is: 5599 with ccp_alpha: 2.362948960302456e-05 and depth of 38 Number of nodes in the tree # 100 is: 5581 with ccp_alpha: 2.3629489603024583e-05 and depth of 38 Number of nodes in the tree # 101 is: 5581 with ccp_alpha: 2.3629489603024583e-05 and depth of 38 Number of nodes in the tree # 102 is: 5569 with ccp_alpha: 2.3868171316186442e-05 and depth of 38 Number of nodes in the tree # 103 is: 5569 with ccp_alpha: 2.3868171316186442e-05 and depth of 38 Number of nodes in the tree # 104 is: 5557 with ccp_alpha: 2.4067072743821327e-05 and depth of 38 Number of nodes in the tree # 105 is: 5557 with ccp_alpha: 2.4067072743821327e-05 and depth of 38 Number of nodes in the tree # 106 is: 5551 with ccp_alpha: 2.4379632130104725e-05 and depth of 38 Number of nodes in the tree # 107 is: 5545 with ccp_alpha: 2.4504655884618082e-05 and depth of 38 Number of nodes in the tree # 108 is: 5539 with ccp_alpha: 2.4614051669817264e-05 and depth of 38 Number of nodes in the tree # 109 is: 5527 with ccp_alpha: 2.4873146950552198e-05 and depth of 38 Number of nodes in the tree # 110 is: 5527 with ccp_alpha: 2.4873146950552198e-05 and depth of 38 Number of nodes in the tree # 111 is: 5521 with ccp_alpha: 2.4891092944023008e-05 and depth of 38 Number of nodes in the tree # 112 is: 5515 with ccp_alpha: 2.4942239025414824e-05 and depth of 38 Number of nodes in the tree # 113 is: 5503 with ccp_alpha: 2.5004750902671514e-05 and depth of 38 Number of nodes in the tree # 114 is: 5503 with ccp_alpha: 2.5004750902671514e-05 and depth of 38 Number of nodes in the tree # 115 is: 5501 with ccp_alpha: 2.501945957967313e-05 and depth of 38 Number of nodes in the tree # 116 is: 5495 with ccp_alpha: 2.5061579881995774e-05 and depth of 38 Number of nodes in the tree # 117 is: 5483 with ccp_alpha: 2.5148310155673755e-05 and depth of 38 Number of nodes in the tree # 118 is: 5477 with ccp_alpha: 2.516103059581322e-05 and depth of 38 Number of nodes in the tree # 119 is: 5465 with ccp_alpha: 2.5204788909892872e-05 and depth of 38 Number of nodes in the tree # 120 is: 5465 with ccp_alpha: 2.5204788909892872e-05 and depth of 38 Number of nodes in the tree # 121 is: 5459 with ccp_alpha: 2.5245181199812594e-05 and depth of 38 Number of nodes in the tree # 122 is: 5447 with ccp_alpha: 2.53173102889549e-05 and depth of 38 Number of nodes in the tree # 123 is: 5447 with ccp_alpha: 2.53173102889549e-05 and depth of 38 Number of nodes in the tree # 124 is: 5435 with ccp_alpha: 2.5364988839404917e-05 and depth of 38 Number of nodes in the tree # 125 is: 5429 with ccp_alpha: 2.5434520058811175e-05 and depth of 38 Number of nodes in the tree # 126 is: 5417 with ccp_alpha: 2.545938273726552e-05 and depth of 38 Number of nodes in the tree # 127 is: 5417 with ccp_alpha: 2.545938273726552e-05 and depth of 38 Number of nodes in the tree # 128 is: 5411 with ccp_alpha: 2.5482782905222573e-05 and depth of 38 Number of nodes in the tree # 129 is: 5405 with ccp_alpha: 2.552568321314384e-05 and depth of 38 Number of nodes in the tree # 130 is: 5393 with ccp_alpha: 2.5535673695810437e-05 and depth of 38 Number of nodes in the tree # 131 is: 5387 with ccp_alpha: 2.5598613736609936e-05 and depth of 38 Number of nodes in the tree # 132 is: 5381 with ccp_alpha: 2.5614622875907407e-05 and depth of 38 Number of nodes in the tree # 133 is: 5375 with ccp_alpha: 2.5629869675238282e-05 and depth of 38 Number of nodes in the tree # 134 is: 5369 with ccp_alpha: 2.5684227829374514e-05 and depth of 38 Number of nodes in the tree # 135 is: 5363 with ccp_alpha: 2.5817405307008347e-05 and depth of 38 Number of nodes in the tree # 136 is: 5357 with ccp_alpha: 2.5828063169813805e-05 and depth of 38 Number of nodes in the tree # 137 is: 5345 with ccp_alpha: 2.5844754253308128e-05 and depth of 38 Number of nodes in the tree # 138 is: 5339 with ccp_alpha: 2.5863122948584086e-05 and depth of 38 Number of nodes in the tree # 139 is: 5333 with ccp_alpha: 2.5922646821883493e-05 and depth of 38 Number of nodes in the tree # 140 is: 5327 with ccp_alpha: 2.599758660027755e-05 and depth of 38 Number of nodes in the tree # 141 is: 5321 with ccp_alpha: 2.603248854570497e-05 and depth of 38 Number of nodes in the tree # 142 is: 5315 with ccp_alpha: 2.608450150983232e-05 and depth of 38 Number of nodes in the tree # 143 is: 5309 with ccp_alpha: 2.62249484152561e-05 and depth of 38 Number of nodes in the tree # 144 is: 5229 with ccp_alpha: 2.625498844780508e-05 and depth of 38 Number of nodes in the tree # 145 is: 5229 with ccp_alpha: 2.625498844780508e-05 and depth of 38 Number of nodes in the tree # 146 is: 5229 with ccp_alpha: 2.625498844780508e-05 and depth of 38 Number of nodes in the tree # 147 is: 5229 with ccp_alpha: 2.625498844780508e-05 and depth of 38 Number of nodes in the tree # 148 is: 5229 with ccp_alpha: 2.625498844780508e-05 and depth of 38 Number of nodes in the tree # 149 is: 5229 with ccp_alpha: 2.625498844780508e-05 and depth of 38 Number of nodes in the tree # 150 is: 5229 with ccp_alpha: 2.625498844780508e-05 and depth of 38 Number of nodes in the tree # 151 is: 5229 with ccp_alpha: 2.625498844780508e-05 and depth of 38 Number of nodes in the tree # 152 is: 5229 with ccp_alpha: 2.625498844780508e-05 and depth of 38 Number of nodes in the tree # 153 is: 5229 with ccp_alpha: 2.625498844780508e-05 and depth of 38 Number of nodes in the tree # 154 is: 5229 with ccp_alpha: 2.625498844780508e-05 and depth of 38 Number of nodes in the tree # 155 is: 5229 with ccp_alpha: 2.625498844780508e-05 and depth of 38 Number of nodes in the tree # 156 is: 5229 with ccp_alpha: 2.625498844780508e-05 and depth of 38 Number of nodes in the tree # 157 is: 5229 with ccp_alpha: 2.625498844780508e-05 and depth of 38 Number of nodes in the tree # 158 is: 5229 with ccp_alpha: 2.625498844780508e-05 and depth of 38 Number of nodes in the tree # 159 is: 5229 with ccp_alpha: 2.625498844780508e-05 and depth of 38 Number of nodes in the tree # 160 is: 5229 with ccp_alpha: 2.625498844780508e-05 and depth of 38 Number of nodes in the tree # 161 is: 5229 with ccp_alpha: 2.625498844780508e-05 and depth of 38 Number of nodes in the tree # 162 is: 5229 with ccp_alpha: 2.625498844780508e-05 and depth of 38 Number of nodes in the tree # 163 is: 5229 with ccp_alpha: 2.625498844780508e-05 and depth of 38 Number of nodes in the tree # 164 is: 5213 with ccp_alpha: 2.6254988447805088e-05 and depth of 38 Number of nodes in the tree # 165 is: 5213 with ccp_alpha: 2.6254988447805088e-05 and depth of 38 Number of nodes in the tree # 166 is: 5213 with ccp_alpha: 2.6254988447805088e-05 and depth of 38 Number of nodes in the tree # 167 is: 5213 with ccp_alpha: 2.6254988447805088e-05 and depth of 38 Number of nodes in the tree # 168 is: 5213 with ccp_alpha: 2.6254988447805088e-05 and depth of 38 Number of nodes in the tree # 169 is: 5213 with ccp_alpha: 2.6254988447805088e-05 and depth of 38 Number of nodes in the tree # 170 is: 5213 with ccp_alpha: 2.6254988447805088e-05 and depth of 38 Number of nodes in the tree # 171 is: 5213 with ccp_alpha: 2.6254988447805088e-05 and depth of 38 Number of nodes in the tree # 172 is: 5205 with ccp_alpha: 2.6723827527230176e-05 and depth of 38 Number of nodes in the tree # 173 is: 5199 with ccp_alpha: 2.746129872783941e-05 and depth of 38 Number of nodes in the tree # 174 is: 5197 with ccp_alpha: 2.8130344765505455e-05 and depth of 38 Number of nodes in the tree # 175 is: 5187 with ccp_alpha: 2.864180557942373e-05 and depth of 38 Number of nodes in the tree # 176 is: 5183 with ccp_alpha: 2.9536862003780716e-05 and depth of 38 Number of nodes in the tree # 177 is: 5183 with ccp_alpha: 2.9536862003780716e-05 and depth of 38 Number of nodes in the tree # 178 is: 5135 with ccp_alpha: 2.953686200378072e-05 and depth of 38 Number of nodes in the tree # 179 is: 5135 with ccp_alpha: 2.953686200378072e-05 and depth of 38 Number of nodes in the tree # 180 is: 5135 with ccp_alpha: 2.953686200378072e-05 and depth of 38 Number of nodes in the tree # 181 is: 5135 with ccp_alpha: 2.953686200378072e-05 and depth of 38 Number of nodes in the tree # 182 is: 5135 with ccp_alpha: 2.953686200378072e-05 and depth of 38 Number of nodes in the tree # 183 is: 5135 with ccp_alpha: 2.953686200378072e-05 and depth of 38 Number of nodes in the tree # 184 is: 5135 with ccp_alpha: 2.953686200378072e-05 and depth of 38 Number of nodes in the tree # 185 is: 5135 with ccp_alpha: 2.953686200378072e-05 and depth of 38 Number of nodes in the tree # 186 is: 5135 with ccp_alpha: 2.953686200378072e-05 and depth of 38 Number of nodes in the tree # 187 is: 5135 with ccp_alpha: 2.953686200378072e-05 and depth of 38 Number of nodes in the tree # 188 is: 5135 with ccp_alpha: 2.953686200378072e-05 and depth of 38 Number of nodes in the tree # 189 is: 5135 with ccp_alpha: 2.953686200378072e-05 and depth of 38 Number of nodes in the tree # 190 is: 5133 with ccp_alpha: 3.0005701083205807e-05 and depth of 38 Number of nodes in the tree # 191 is: 5127 with ccp_alpha: 3.000570108320581e-05 and depth of 38 Number of nodes in the tree # 192 is: 5123 with ccp_alpha: 3.0005701083205814e-05 and depth of 38 Number of nodes in the tree # 193 is: 5113 with ccp_alpha: 3.0040591433302557e-05 and depth of 38 Number of nodes in the tree # 194 is: 5111 with ccp_alpha: 3.013965510589888e-05 and depth of 38 Number of nodes in the tree # 195 is: 5089 with ccp_alpha: 3.021262619053019e-05 and depth of 38 Number of nodes in the tree # 196 is: 5079 with ccp_alpha: 3.0213002295885928e-05 and depth of 38 Number of nodes in the tree # 197 is: 5069 with ccp_alpha: 3.02704572692341e-05 and depth of 38 Number of nodes in the tree # 198 is: 5059 with ccp_alpha: 3.040051293956379e-05 and depth of 38 Number of nodes in the tree # 199 is: 5057 with ccp_alpha: 3.0630819855772596e-05 and depth of 38 Number of nodes in the tree # 200 is: 5037 with ccp_alpha: 3.1033986345045996e-05 and depth of 38 Number of nodes in the tree # 201 is: 5027 with ccp_alpha: 3.1097074071923986e-05 and depth of 38 Number of nodes in the tree # 202 is: 5025 with ccp_alpha: 3.150598613736608e-05 and depth of 38 Number of nodes in the tree # 203 is: 4989 with ccp_alpha: 3.150598613736609e-05 and depth of 38 Number of nodes in the tree # 204 is: 4989 with ccp_alpha: 3.150598613736609e-05 and depth of 38 Number of nodes in the tree # 205 is: 4989 with ccp_alpha: 3.150598613736609e-05 and depth of 38 Number of nodes in the tree # 206 is: 4989 with ccp_alpha: 3.150598613736609e-05 and depth of 38 Number of nodes in the tree # 207 is: 4989 with ccp_alpha: 3.150598613736609e-05 and depth of 38 Number of nodes in the tree # 208 is: 4989 with ccp_alpha: 3.150598613736609e-05 and depth of 38 Number of nodes in the tree # 209 is: 4989 with ccp_alpha: 3.150598613736609e-05 and depth of 38 Number of nodes in the tree # 210 is: 4989 with ccp_alpha: 3.150598613736609e-05 and depth of 38 Number of nodes in the tree # 211 is: 4989 with ccp_alpha: 3.150598613736609e-05 and depth of 38 Number of nodes in the tree # 212 is: 4985 with ccp_alpha: 3.150598613736612e-05 and depth of 38 Number of nodes in the tree # 213 is: 4985 with ccp_alpha: 3.150598613736612e-05 and depth of 38 Number of nodes in the tree # 214 is: 4977 with ccp_alpha: 3.22220312768517e-05 and depth of 38 Number of nodes in the tree # 215 is: 4975 with ccp_alpha: 3.2222031276851705e-05 and depth of 38 Number of nodes in the tree # 216 is: 4961 with ccp_alpha: 3.230971027352339e-05 and depth of 38 Number of nodes in the tree # 217 is: 4949 with ccp_alpha: 3.281873555975635e-05 and depth of 38 Number of nodes in the tree # 218 is: 4893 with ccp_alpha: 3.281873555975636e-05 and depth of 38 Number of nodes in the tree # 219 is: 4893 with ccp_alpha: 3.281873555975636e-05 and depth of 38 Number of nodes in the tree # 220 is: 4893 with ccp_alpha: 3.281873555975636e-05 and depth of 38 Number of nodes in the tree # 221 is: 4893 with ccp_alpha: 3.281873555975636e-05 and depth of 38 Number of nodes in the tree # 222 is: 4893 with ccp_alpha: 3.281873555975636e-05 and depth of 38 Number of nodes in the tree # 223 is: 4893 with ccp_alpha: 3.281873555975636e-05 and depth of 38 Number of nodes in the tree # 224 is: 4893 with ccp_alpha: 3.281873555975636e-05 and depth of 38 Number of nodes in the tree # 225 is: 4893 with ccp_alpha: 3.281873555975636e-05 and depth of 38 Number of nodes in the tree # 226 is: 4893 with ccp_alpha: 3.281873555975636e-05 and depth of 38 Number of nodes in the tree # 227 is: 4893 with ccp_alpha: 3.281873555975636e-05 and depth of 38 Number of nodes in the tree # 228 is: 4893 with ccp_alpha: 3.281873555975636e-05 and depth of 38 Number of nodes in the tree # 229 is: 4893 with ccp_alpha: 3.281873555975636e-05 and depth of 38 Number of nodes in the tree # 230 is: 4893 with ccp_alpha: 3.281873555975636e-05 and depth of 38 Number of nodes in the tree # 231 is: 4893 with ccp_alpha: 3.281873555975636e-05 and depth of 38 Number of nodes in the tree # 232 is: 4879 with ccp_alpha: 3.283578425355357e-05 and depth of 38 Number of nodes in the tree # 233 is: 4871 with ccp_alpha: 3.3323639183752604e-05 and depth of 38 Number of nodes in the tree # 234 is: 4869 with ccp_alpha: 3.356461591338722e-05 and depth of 38 Number of nodes in the tree # 235 is: 4815 with ccp_alpha: 3.375641371860654e-05 and depth of 38 Number of nodes in the tree # 236 is: 4815 with ccp_alpha: 3.375641371860654e-05 and depth of 38 Number of nodes in the tree # 237 is: 4815 with ccp_alpha: 3.375641371860654e-05 and depth of 38 Number of nodes in the tree # 238 is: 4815 with ccp_alpha: 3.375641371860654e-05 and depth of 38 Number of nodes in the tree # 239 is: 4815 with ccp_alpha: 3.375641371860654e-05 and depth of 38 Number of nodes in the tree # 240 is: 4815 with ccp_alpha: 3.375641371860654e-05 and depth of 38 Number of nodes in the tree # 241 is: 4815 with ccp_alpha: 3.375641371860654e-05 and depth of 38 Number of nodes in the tree # 242 is: 4815 with ccp_alpha: 3.375641371860654e-05 and depth of 38 Number of nodes in the tree # 243 is: 4815 with ccp_alpha: 3.375641371860654e-05 and depth of 38 Number of nodes in the tree # 244 is: 4815 with ccp_alpha: 3.375641371860654e-05 and depth of 38 Number of nodes in the tree # 245 is: 4815 with ccp_alpha: 3.375641371860654e-05 and depth of 38 Number of nodes in the tree # 246 is: 4815 with ccp_alpha: 3.375641371860654e-05 and depth of 38 Number of nodes in the tree # 247 is: 4815 with ccp_alpha: 3.375641371860654e-05 and depth of 38 Number of nodes in the tree # 248 is: 4815 with ccp_alpha: 3.375641371860654e-05 and depth of 38 Number of nodes in the tree # 249 is: 4807 with ccp_alpha: 3.380329762654904e-05 and depth of 38 Number of nodes in the tree # 250 is: 4803 with ccp_alpha: 3.406897310488992e-05 and depth of 38 Number of nodes in the tree # 251 is: 4799 with ccp_alpha: 3.429479352273103e-05 and depth of 38 Number of nodes in the tree # 252 is: 4797 with ccp_alpha: 3.43701666953085e-05 and depth of 38 Number of nodes in the tree # 253 is: 4757 with ccp_alpha: 3.445967233774417e-05 and depth of 38 Number of nodes in the tree # 254 is: 4757 with ccp_alpha: 3.445967233774417e-05 and depth of 38 Number of nodes in the tree # 255 is: 4757 with ccp_alpha: 3.445967233774417e-05 and depth of 38 Number of nodes in the tree # 256 is: 4757 with ccp_alpha: 3.445967233774417e-05 and depth of 38 Number of nodes in the tree # 257 is: 4757 with ccp_alpha: 3.445967233774417e-05 and depth of 38 Number of nodes in the tree # 258 is: 4757 with ccp_alpha: 3.445967233774417e-05 and depth of 38 Number of nodes in the tree # 259 is: 4757 with ccp_alpha: 3.445967233774417e-05 and depth of 38 Number of nodes in the tree # 260 is: 4757 with ccp_alpha: 3.445967233774417e-05 and depth of 38 Number of nodes in the tree # 261 is: 4757 with ccp_alpha: 3.445967233774417e-05 and depth of 38 Number of nodes in the tree # 262 is: 4757 with ccp_alpha: 3.445967233774417e-05 and depth of 38 Number of nodes in the tree # 263 is: 4749 with ccp_alpha: 3.448613905996978e-05 and depth of 38 Number of nodes in the tree # 264 is: 4741 with ccp_alpha: 3.4650478939835784e-05 and depth of 38 Number of nodes in the tree # 265 is: 4737 with ccp_alpha: 3.469409187745672e-05 and depth of 38 Number of nodes in the tree # 266 is: 4689 with ccp_alpha: 3.500665126374011e-05 and depth of 38 Number of nodes in the tree # 267 is: 4689 with ccp_alpha: 3.500665126374011e-05 and depth of 38 Number of nodes in the tree # 268 is: 4689 with ccp_alpha: 3.500665126374011e-05 and depth of 38 Number of nodes in the tree # 269 is: 4689 with ccp_alpha: 3.500665126374011e-05 and depth of 38 Number of nodes in the tree # 270 is: 4689 with ccp_alpha: 3.500665126374011e-05 and depth of 38 Number of nodes in the tree # 271 is: 4689 with ccp_alpha: 3.500665126374011e-05 and depth of 38 Number of nodes in the tree # 272 is: 4689 with ccp_alpha: 3.500665126374011e-05 and depth of 38 Number of nodes in the tree # 273 is: 4689 with ccp_alpha: 3.500665126374011e-05 and depth of 38 Number of nodes in the tree # 274 is: 4689 with ccp_alpha: 3.500665126374011e-05 and depth of 38 Number of nodes in the tree # 275 is: 4689 with ccp_alpha: 3.500665126374011e-05 and depth of 38 Number of nodes in the tree # 276 is: 4689 with ccp_alpha: 3.500665126374011e-05 and depth of 38 Number of nodes in the tree # 277 is: 4683 with ccp_alpha: 3.508731175052291e-05 and depth of 38 Number of nodes in the tree # 278 is: 4675 with ccp_alpha: 3.512334010128592e-05 and depth of 38 Number of nodes in the tree # 279 is: 4675 with ccp_alpha: 3.512334010128592e-05 and depth of 38 Number of nodes in the tree # 280 is: 4671 with ccp_alpha: 3.5141292230139116e-05 and depth of 38 Number of nodes in the tree # 281 is: 4661 with ccp_alpha: 3.5158393804500285e-05 and depth of 38 Number of nodes in the tree # 282 is: 4657 with ccp_alpha: 3.51629309568818e-05 and depth of 38 Number of nodes in the tree # 283 is: 4657 with ccp_alpha: 3.51629309568818e-05 and depth of 38 Number of nodes in the tree # 284 is: 4655 with ccp_alpha: 3.523695817994897e-05 and depth of 38 Number of nodes in the tree # 285 is: 4651 with ccp_alpha: 3.527112459059529e-05 and depth of 38 Number of nodes in the tree # 286 is: 4643 with ccp_alpha: 3.544423440453685e-05 and depth of 38 Number of nodes in the tree # 287 is: 4643 with ccp_alpha: 3.544423440453685e-05 and depth of 38 Number of nodes in the tree # 288 is: 4643 with ccp_alpha: 3.544423440453685e-05 and depth of 38 Number of nodes in the tree # 289 is: 4643 with ccp_alpha: 3.544423440453685e-05 and depth of 38 Number of nodes in the tree # 290 is: 4595 with ccp_alpha: 3.544423440453687e-05 and depth of 38 Number of nodes in the tree # 291 is: 4595 with ccp_alpha: 3.544423440453687e-05 and depth of 38 Number of nodes in the tree # 292 is: 4595 with ccp_alpha: 3.544423440453687e-05 and depth of 38 Number of nodes in the tree # 293 is: 4595 with ccp_alpha: 3.544423440453687e-05 and depth of 38 Number of nodes in the tree # 294 is: 4595 with ccp_alpha: 3.544423440453687e-05 and depth of 38 Number of nodes in the tree # 295 is: 4595 with ccp_alpha: 3.544423440453687e-05 and depth of 38 Number of nodes in the tree # 296 is: 4595 with ccp_alpha: 3.544423440453687e-05 and depth of 38 Number of nodes in the tree # 297 is: 4595 with ccp_alpha: 3.544423440453687e-05 and depth of 38 Number of nodes in the tree # 298 is: 4595 with ccp_alpha: 3.544423440453687e-05 and depth of 38 Number of nodes in the tree # 299 is: 4595 with ccp_alpha: 3.544423440453687e-05 and depth of 38 Number of nodes in the tree # 300 is: 4595 with ccp_alpha: 3.544423440453687e-05 and depth of 38 Number of nodes in the tree # 301 is: 4595 with ccp_alpha: 3.544423440453687e-05 and depth of 38 Number of nodes in the tree # 302 is: 4581 with ccp_alpha: 3.550365703204542e-05 and depth of 38 Number of nodes in the tree # 303 is: 4577 with ccp_alpha: 3.5538002220421896e-05 and depth of 38 Number of nodes in the tree # 304 is: 4569 with ccp_alpha: 3.56317700363069e-05 and depth of 38 Number of nodes in the tree # 305 is: 4555 with ccp_alpha: 3.5667453348905815e-05 and depth of 38 Number of nodes in the tree # 306 is: 4507 with ccp_alpha: 3.5802256974279665e-05 and depth of 38 Number of nodes in the tree # 307 is: 4507 with ccp_alpha: 3.5802256974279665e-05 and depth of 38 Number of nodes in the tree # 308 is: 4507 with ccp_alpha: 3.5802256974279665e-05 and depth of 38 Number of nodes in the tree # 309 is: 4507 with ccp_alpha: 3.5802256974279665e-05 and depth of 38 Number of nodes in the tree # 310 is: 4507 with ccp_alpha: 3.5802256974279665e-05 and depth of 38 Number of nodes in the tree # 311 is: 4507 with ccp_alpha: 3.5802256974279665e-05 and depth of 38 Number of nodes in the tree # 312 is: 4507 with ccp_alpha: 3.5802256974279665e-05 and depth of 38 Number of nodes in the tree # 313 is: 4507 with ccp_alpha: 3.5802256974279665e-05 and depth of 38 Number of nodes in the tree # 314 is: 4507 with ccp_alpha: 3.5802256974279665e-05 and depth of 38 Number of nodes in the tree # 315 is: 4507 with ccp_alpha: 3.5802256974279665e-05 and depth of 38 Number of nodes in the tree # 316 is: 4507 with ccp_alpha: 3.5802256974279665e-05 and depth of 38 Number of nodes in the tree # 317 is: 4507 with ccp_alpha: 3.5802256974279665e-05 and depth of 38 Number of nodes in the tree # 318 is: 4503 with ccp_alpha: 3.5921597830860585e-05 and depth of 38 Number of nodes in the tree # 319 is: 4499 with ccp_alpha: 3.6006841299846974e-05 and depth of 38 Number of nodes in the tree # 320 is: 4479 with ccp_alpha: 3.604210615234286e-05 and depth of 38 Number of nodes in the tree # 321 is: 4475 with ccp_alpha: 3.6075036075036075e-05 and depth of 38 Number of nodes in the tree # 322 is: 4429 with ccp_alpha: 3.610060911573199e-05 and depth of 38 Number of nodes in the tree # 323 is: 4429 with ccp_alpha: 3.610060911573199e-05 and depth of 38 Number of nodes in the tree # 324 is: 4429 with ccp_alpha: 3.610060911573199e-05 and depth of 38 Number of nodes in the tree # 325 is: 4429 with ccp_alpha: 3.610060911573199e-05 and depth of 38 Number of nodes in the tree # 326 is: 4429 with ccp_alpha: 3.610060911573199e-05 and depth of 38 Number of nodes in the tree # 327 is: 4429 with ccp_alpha: 3.610060911573199e-05 and depth of 38 Number of nodes in the tree # 328 is: 4429 with ccp_alpha: 3.610060911573199e-05 and depth of 38 Number of nodes in the tree # 329 is: 4429 with ccp_alpha: 3.610060911573199e-05 and depth of 38 Number of nodes in the tree # 330 is: 4429 with ccp_alpha: 3.610060911573199e-05 and depth of 38 Number of nodes in the tree # 331 is: 4429 with ccp_alpha: 3.610060911573199e-05 and depth of 38 Number of nodes in the tree # 332 is: 4429 with ccp_alpha: 3.610060911573199e-05 and depth of 38 Number of nodes in the tree # 333 is: 4421 with ccp_alpha: 3.6231884057971014e-05 and depth of 38 Number of nodes in the tree # 334 is: 4417 with ccp_alpha: 3.624195126665614e-05 and depth of 38 Number of nodes in the tree # 335 is: 4405 with ccp_alpha: 3.627333930288862e-05 and depth of 38 Number of nodes in the tree # 336 is: 4397 with ccp_alpha: 3.632515835903555e-05 and depth of 38 Number of nodes in the tree # 337 is: 4361 with ccp_alpha: 3.63530609277301e-05 and depth of 38 Number of nodes in the tree # 338 is: 4361 with ccp_alpha: 3.63530609277301e-05 and depth of 38 Number of nodes in the tree # 339 is: 4361 with ccp_alpha: 3.63530609277301e-05 and depth of 38 Number of nodes in the tree # 340 is: 4361 with ccp_alpha: 3.63530609277301e-05 and depth of 38 Number of nodes in the tree # 341 is: 4361 with ccp_alpha: 3.63530609277301e-05 and depth of 38 Number of nodes in the tree # 342 is: 4361 with ccp_alpha: 3.63530609277301e-05 and depth of 38 Number of nodes in the tree # 343 is: 4361 with ccp_alpha: 3.63530609277301e-05 and depth of 38 Number of nodes in the tree # 344 is: 4361 with ccp_alpha: 3.63530609277301e-05 and depth of 38 Number of nodes in the tree # 345 is: 4361 with ccp_alpha: 3.63530609277301e-05 and depth of 38 Number of nodes in the tree # 346 is: 4357 with ccp_alpha: 3.6395266493822736e-05 and depth of 38 Number of nodes in the tree # 347 is: 4349 with ccp_alpha: 3.6556563307003955e-05 and depth of 38 Number of nodes in the tree # 348 is: 4329 with ccp_alpha: 3.656944819515709e-05 and depth of 38 Number of nodes in the tree # 349 is: 4329 with ccp_alpha: 3.656944819515709e-05 and depth of 38 Number of nodes in the tree # 350 is: 4329 with ccp_alpha: 3.656944819515709e-05 and depth of 38 Number of nodes in the tree # 351 is: 4329 with ccp_alpha: 3.656944819515709e-05 and depth of 38 Number of nodes in the tree # 352 is: 4329 with ccp_alpha: 3.656944819515709e-05 and depth of 38 Number of nodes in the tree # 353 is: 4279 with ccp_alpha: 3.6676930738052384e-05 and depth of 38 Number of nodes in the tree # 354 is: 4263 with ccp_alpha: 3.6756983826927123e-05 and depth of 38 Number of nodes in the tree # 355 is: 4263 with ccp_alpha: 3.6756983826927123e-05 and depth of 38 Number of nodes in the tree # 356 is: 4263 with ccp_alpha: 3.6756983826927123e-05 and depth of 38 Number of nodes in the tree # 357 is: 4263 with ccp_alpha: 3.6756983826927123e-05 and depth of 38 Number of nodes in the tree # 358 is: 4251 with ccp_alpha: 3.681405988877019e-05 and depth of 38 Number of nodes in the tree # 359 is: 4235 with ccp_alpha: 3.69210775047259e-05 and depth of 38 Number of nodes in the tree # 360 is: 4235 with ccp_alpha: 3.69210775047259e-05 and depth of 38 Number of nodes in the tree # 361 is: 4235 with ccp_alpha: 3.69210775047259e-05 and depth of 38 Number of nodes in the tree # 362 is: 4223 with ccp_alpha: 3.697131026323573e-05 and depth of 38 Number of nodes in the tree # 363 is: 4211 with ccp_alpha: 3.6985330142522565e-05 and depth of 38 Number of nodes in the tree # 364 is: 4199 with ccp_alpha: 3.706586604396012e-05 and depth of 38 Number of nodes in the tree # 365 is: 4199 with ccp_alpha: 3.706586604396012e-05 and depth of 38 Number of nodes in the tree # 366 is: 4191 with ccp_alpha: 3.718017278546084e-05 and depth of 38 Number of nodes in the tree # 367 is: 4185 with ccp_alpha: 3.719456696772387e-05 and depth of 38 Number of nodes in the tree # 368 is: 4177 with ccp_alpha: 3.719456696772388e-05 and depth of 38 Number of nodes in the tree # 369 is: 4177 with ccp_alpha: 3.719456696772388e-05 and depth of 38 Number of nodes in the tree # 370 is: 4167 with ccp_alpha: 3.729730387904135e-05 and depth of 38 Number of nodes in the tree # 371 is: 4151 with ccp_alpha: 3.73097204258283e-05 and depth of 38 Number of nodes in the tree # 372 is: 4151 with ccp_alpha: 3.73097204258283e-05 and depth of 38 Number of nodes in the tree # 373 is: 4151 with ccp_alpha: 3.73097204258283e-05 and depth of 38 Number of nodes in the tree # 374 is: 4151 with ccp_alpha: 3.73097204258283e-05 and depth of 38 Number of nodes in the tree # 375 is: 4143 with ccp_alpha: 3.750281918023495e-05 and depth of 38 Number of nodes in the tree # 376 is: 4139 with ccp_alpha: 3.7556278838140536e-05 and depth of 38 Number of nodes in the tree # 377 is: 4127 with ccp_alpha: 3.759236982299366e-05 and depth of 38 Number of nodes in the tree # 378 is: 4127 with ccp_alpha: 3.759236982299366e-05 and depth of 38 Number of nodes in the tree # 379 is: 4127 with ccp_alpha: 3.759236982299366e-05 and depth of 38 Number of nodes in the tree # 380 is: 4109 with ccp_alpha: 3.765573340071564e-05 and depth of 38 Number of nodes in the tree # 381 is: 4093 with ccp_alpha: 3.7670200816416e-05 and depth of 38 Number of nodes in the tree # 382 is: 4093 with ccp_alpha: 3.7670200816416e-05 and depth of 38 Number of nodes in the tree # 383 is: 4093 with ccp_alpha: 3.7670200816416e-05 and depth of 38 Number of nodes in the tree # 384 is: 4089 with ccp_alpha: 3.774154589371983e-05 and depth of 38 Number of nodes in the tree # 385 is: 4077 with ccp_alpha: 3.780718336483931e-05 and depth of 38 Number of nodes in the tree # 386 is: 4077 with ccp_alpha: 3.780718336483931e-05 and depth of 38 Number of nodes in the tree # 387 is: 4065 with ccp_alpha: 3.786777179971889e-05 and depth of 38 Number of nodes in the tree # 388 is: 4065 with ccp_alpha: 3.786777179971889e-05 and depth of 38 Number of nodes in the tree # 389 is: 4065 with ccp_alpha: 3.786777179971889e-05 and depth of 38 Number of nodes in the tree # 390 is: 4057 with ccp_alpha: 3.786988184471475e-05 and depth of 38 Number of nodes in the tree # 391 is: 4045 with ccp_alpha: 3.7912141213394716e-05 and depth of 38 Number of nodes in the tree # 392 is: 4041 with ccp_alpha: 3.792387220238511e-05 and depth of 38 Number of nodes in the tree # 393 is: 4033 with ccp_alpha: 3.797596543343235e-05 and depth of 38 Number of nodes in the tree # 394 is: 4033 with ccp_alpha: 3.797596543343235e-05 and depth of 38 Number of nodes in the tree # 395 is: 4025 with ccp_alpha: 3.802446602785561e-05 and depth of 38 Number of nodes in the tree # 396 is: 4025 with ccp_alpha: 3.802446602785561e-05 and depth of 38 Number of nodes in the tree # 397 is: 4021 with ccp_alpha: 3.8069733249317354e-05 and depth of 38 Number of nodes in the tree # 398 is: 4013 with ccp_alpha: 3.811208000487832e-05 and depth of 38 Number of nodes in the tree # 399 is: 4013 with ccp_alpha: 3.811208000487832e-05 and depth of 38 Number of nodes in the tree # 400 is: 4005 with ccp_alpha: 3.817489559159289e-05 and depth of 38 Number of nodes in the tree # 401 is: 4001 with ccp_alpha: 3.825726888108741e-05 and depth of 38 Number of nodes in the tree # 402 is: 3997 with ccp_alpha: 3.8318091248147996e-05 and depth of 38 Number of nodes in the tree # 403 is: 3993 with ccp_alpha: 3.834116519510483e-05 and depth of 38 Number of nodes in the tree # 404 is: 3989 with ccp_alpha: 3.834610154876792e-05 and depth of 38 Number of nodes in the tree # 405 is: 3985 with ccp_alpha: 3.8435501381062346e-05 and depth of 38 Number of nodes in the tree # 406 is: 3977 with ccp_alpha: 3.84558360206086e-05 and depth of 38 Number of nodes in the tree # 407 is: 3973 with ccp_alpha: 3.846661098166794e-05 and depth of 38 Number of nodes in the tree # 408 is: 3969 with ccp_alpha: 3.850731639011415e-05 and depth of 38 Number of nodes in the tree # 409 is: 3965 with ccp_alpha: 3.852634174406177e-05 and depth of 38 Number of nodes in the tree # 410 is: 3961 with ccp_alpha: 3.857147133335151e-05 and depth of 38 Number of nodes in the tree # 411 is: 3953 with ccp_alpha: 3.8594833018273484e-05 and depth of 38 Number of nodes in the tree # 412 is: 3953 with ccp_alpha: 3.8594833018273484e-05 and depth of 38 Number of nodes in the tree # 413 is: 3949 with ccp_alpha: 3.867922405257001e-05 and depth of 38 Number of nodes in the tree # 414 is: 3945 with ccp_alpha: 3.872610796051252e-05 and depth of 38 Number of nodes in the tree # 415 is: 3941 with ccp_alpha: 3.876713137996219e-05 and depth of 38 Number of nodes in the tree # 416 is: 3937 with ccp_alpha: 3.877659832291214e-05 and depth of 38 Number of nodes in the tree # 417 is: 3933 with ccp_alpha: 3.882779981717653e-05 and depth of 38 Number of nodes in the tree # 418 is: 3929 with ccp_alpha: 3.886429211023779e-05 and depth of 38 Number of nodes in the tree # 419 is: 3921 with ccp_alpha: 3.898467981643777e-05 and depth of 38 Number of nodes in the tree # 420 is: 3921 with ccp_alpha: 3.898467981643777e-05 and depth of 38 Number of nodes in the tree # 421 is: 3915 with ccp_alpha: 3.915217575549882e-05 and depth of 38 Number of nodes in the tree # 422 is: 3911 with ccp_alpha: 3.938248267170761e-05 and depth of 38 Number of nodes in the tree # 423 is: 3865 with ccp_alpha: 3.9382482671707626e-05 and depth of 38 Number of nodes in the tree # 424 is: 3865 with ccp_alpha: 3.9382482671707626e-05 and depth of 38 Number of nodes in the tree # 425 is: 3865 with ccp_alpha: 3.9382482671707626e-05 and depth of 38 Number of nodes in the tree # 426 is: 3865 with ccp_alpha: 3.9382482671707626e-05 and depth of 38 Number of nodes in the tree # 427 is: 3865 with ccp_alpha: 3.9382482671707626e-05 and depth of 38 Number of nodes in the tree # 428 is: 3865 with ccp_alpha: 3.9382482671707626e-05 and depth of 38 Number of nodes in the tree # 429 is: 3865 with ccp_alpha: 3.9382482671707626e-05 and depth of 38 Number of nodes in the tree # 430 is: 3865 with ccp_alpha: 3.9382482671707626e-05 and depth of 38 Number of nodes in the tree # 431 is: 3865 with ccp_alpha: 3.9382482671707626e-05 and depth of 38 Number of nodes in the tree # 432 is: 3865 with ccp_alpha: 3.9382482671707626e-05 and depth of 38 Number of nodes in the tree # 433 is: 3861 with ccp_alpha: 3.938248267170763e-05 and depth of 38 Number of nodes in the tree # 434 is: 3851 with ccp_alpha: 3.9404361828747476e-05 and depth of 38 Number of nodes in the tree # 435 is: 3839 with ccp_alpha: 3.9713427904242974e-05 and depth of 38 Number of nodes in the tree # 436 is: 3835 with ccp_alpha: 3.986460932524358e-05 and depth of 38 Number of nodes in the tree # 437 is: 3831 with ccp_alpha: 4.011267459966339e-05 and depth of 38 Number of nodes in the tree # 438 is: 3829 with ccp_alpha: 4.050769646232784e-05 and depth of 38 Number of nodes in the tree # 439 is: 3819 with ccp_alpha: 4.079621281889712e-05 and depth of 38 Number of nodes in the tree # 440 is: 3809 with ccp_alpha: 4.102341944969544e-05 and depth of 38 Number of nodes in the tree # 441 is: 3809 with ccp_alpha: 4.102341944969544e-05 and depth of 38 Number of nodes in the tree # 442 is: 3809 with ccp_alpha: 4.102341944969544e-05 and depth of 38 Number of nodes in the tree # 443 is: 3803 with ccp_alpha: 4.105325466384065e-05 and depth of 38 Number of nodes in the tree # 444 is: 3793 with ccp_alpha: 4.112542803192187e-05 and depth of 38 Number of nodes in the tree # 445 is: 3789 with ccp_alpha: 4.115469439193445e-05 and depth of 38 Number of nodes in the tree # 446 is: 3781 with ccp_alpha: 4.1257838989407985e-05 and depth of 38 Number of nodes in the tree # 447 is: 3781 with ccp_alpha: 4.1257838989407985e-05 and depth of 38 Number of nodes in the tree # 448 is: 3769 with ccp_alpha: 4.200798151648812e-05 and depth of 38 Number of nodes in the tree # 449 is: 3769 with ccp_alpha: 4.200798151648812e-05 and depth of 38 Number of nodes in the tree # 450 is: 3763 with ccp_alpha: 4.200798151648813e-05 and depth of 38 Number of nodes in the tree # 451 is: 3763 with ccp_alpha: 4.200798151648813e-05 and depth of 38 Number of nodes in the tree # 452 is: 3763 with ccp_alpha: 4.200798151648813e-05 and depth of 38 Number of nodes in the tree # 453 is: 3757 with ccp_alpha: 4.211737730168731e-05 and depth of 38 Number of nodes in the tree # 454 is: 3755 with ccp_alpha: 4.231889585337001e-05 and depth of 38 Number of nodes in the tree # 455 is: 3745 with ccp_alpha: 4.295824271127643e-05 and depth of 38 Number of nodes in the tree # 456 is: 3727 with ccp_alpha: 4.29627083691356e-05 and depth of 38 Number of nodes in the tree # 457 is: 3727 with ccp_alpha: 4.29627083691356e-05 and depth of 38 Number of nodes in the tree # 458 is: 3727 with ccp_alpha: 4.29627083691356e-05 and depth of 38 Number of nodes in the tree # 459 is: 3717 with ccp_alpha: 4.3090005282821925e-05 and depth of 38 Number of nodes in the tree # 460 is: 3703 with ccp_alpha: 4.320523780670786e-05 and depth of 38 Number of nodes in the tree # 461 is: 3699 with ccp_alpha: 4.358897075893331e-05 and depth of 38 Number of nodes in the tree # 462 is: 3697 with ccp_alpha: 4.3758314079675133e-05 and depth of 38 Number of nodes in the tree # 463 is: 3691 with ccp_alpha: 4.375831407967514e-05 and depth of 38 Number of nodes in the tree # 464 is: 3683 with ccp_alpha: 4.388333783418847e-05 and depth of 38 Number of nodes in the tree # 465 is: 3663 with ccp_alpha: 4.418679985787738e-05 and depth of 38 Number of nodes in the tree # 466 is: 3661 with ccp_alpha: 4.430529300567107e-05 and depth of 38 Number of nodes in the tree # 467 is: 3655 with ccp_alpha: 4.434175826740413e-05 and depth of 38 Number of nodes in the tree # 468 is: 3649 with ccp_alpha: 4.468397072366829e-05 and depth of 38 Number of nodes in the tree # 469 is: 3639 with ccp_alpha: 4.4749915516173724e-05 and depth of 38 Number of nodes in the tree # 470 is: 3635 with ccp_alpha: 4.4835441810867175e-05 and depth of 38 Number of nodes in the tree # 471 is: 3619 with ccp_alpha: 4.4975910929405687e-05 and depth of 38 Number of nodes in the tree # 472 is: 3617 with ccp_alpha: 4.4986314198116586e-05 and depth of 38 Number of nodes in the tree # 473 is: 3607 with ccp_alpha: 4.500855162480871e-05 and depth of 38 Number of nodes in the tree # 474 is: 3607 with ccp_alpha: 4.500855162480871e-05 and depth of 38 Number of nodes in the tree # 475 is: 3601 with ccp_alpha: 4.50511733593019e-05 and depth of 38 Number of nodes in the tree # 476 is: 3599 with ccp_alpha: 4.513603559631619e-05 and depth of 38 Number of nodes in the tree # 477 is: 3589 with ccp_alpha: 4.517402424107639e-05 and depth of 38 Number of nodes in the tree # 478 is: 3583 with ccp_alpha: 4.550864664286215e-05 and depth of 38 Number of nodes in the tree # 479 is: 3559 with ccp_alpha: 4.5571403886786735e-05 and depth of 38 Number of nodes in the tree # 480 is: 3555 with ccp_alpha: 4.560703032804934e-05 and depth of 38 Number of nodes in the tree # 481 is: 3551 with ccp_alpha: 4.5826888927077975e-05 and depth of 38 Number of nodes in the tree # 482 is: 3551 with ccp_alpha: 4.5826888927077975e-05 and depth of 38 Number of nodes in the tree # 483 is: 3545 with ccp_alpha: 4.5986998399261055e-05 and depth of 38 Number of nodes in the tree # 484 is: 3539 with ccp_alpha: 4.610077366605284e-05 and depth of 38 Number of nodes in the tree # 485 is: 3533 with ccp_alpha: 4.6102924011795673e-05 and depth of 38 Number of nodes in the tree # 486 is: 3527 with ccp_alpha: 4.614513121129377e-05 and depth of 38 Number of nodes in the tree # 487 is: 3521 with ccp_alpha: 4.633233255495014e-05 and depth of 38 Number of nodes in the tree # 488 is: 3513 with ccp_alpha: 4.6493208709654834e-05 and depth of 38 Number of nodes in the tree # 489 is: 3505 with ccp_alpha: 4.673203049424462e-05 and depth of 38 Number of nodes in the tree # 490 is: 3503 with ccp_alpha: 4.688390794250908e-05 and depth of 38 Number of nodes in the tree # 491 is: 3499 with ccp_alpha: 4.68839079425091e-05 and depth of 38 Number of nodes in the tree # 492 is: 3493 with ccp_alpha: 4.710895070063316e-05 and depth of 38 Number of nodes in the tree # 493 is: 3477 with ccp_alpha: 4.725897920604914e-05 and depth of 38 Number of nodes in the tree # 494 is: 3477 with ccp_alpha: 4.725897920604914e-05 and depth of 38 Number of nodes in the tree # 495 is: 3413 with ccp_alpha: 4.7258979206049145e-05 and depth of 38 Number of nodes in the tree # 496 is: 3413 with ccp_alpha: 4.7258979206049145e-05 and depth of 38 Number of nodes in the tree # 497 is: 3413 with ccp_alpha: 4.7258979206049145e-05 and depth of 38 Number of nodes in the tree # 498 is: 3413 with ccp_alpha: 4.7258979206049145e-05 and depth of 38 Number of nodes in the tree # 499 is: 3413 with ccp_alpha: 4.7258979206049145e-05 and depth of 38 Number of nodes in the tree # 500 is: 3413 with ccp_alpha: 4.7258979206049145e-05 and depth of 38 Number of nodes in the tree # 501 is: 3413 with ccp_alpha: 4.7258979206049145e-05 and depth of 38 Number of nodes in the tree # 502 is: 3413 with ccp_alpha: 4.7258979206049145e-05 and depth of 38 Number of nodes in the tree # 503 is: 3413 with ccp_alpha: 4.7258979206049145e-05 and depth of 38 Number of nodes in the tree # 504 is: 3413 with ccp_alpha: 4.7258979206049145e-05 and depth of 38 Number of nodes in the tree # 505 is: 3413 with ccp_alpha: 4.7258979206049145e-05 and depth of 38 Number of nodes in the tree # 506 is: 3413 with ccp_alpha: 4.7258979206049145e-05 and depth of 38 Number of nodes in the tree # 507 is: 3413 with ccp_alpha: 4.7258979206049145e-05 and depth of 38 Number of nodes in the tree # 508 is: 3413 with ccp_alpha: 4.7258979206049145e-05 and depth of 38 Number of nodes in the tree # 509 is: 3413 with ccp_alpha: 4.7258979206049145e-05 and depth of 38 Number of nodes in the tree # 510 is: 3407 with ccp_alpha: 4.7258979206049166e-05 and depth of 38 Number of nodes in the tree # 511 is: 3405 with ccp_alpha: 4.72589792060492e-05 and depth of 38 Number of nodes in the tree # 512 is: 3393 with ccp_alpha: 4.7576505802969116e-05 and depth of 38 Number of nodes in the tree # 513 is: 3383 with ccp_alpha: 4.773320692555782e-05 and depth of 38 Number of nodes in the tree # 514 is: 3377 with ccp_alpha: 4.7736342632372885e-05 and depth of 38 Number of nodes in the tree # 515 is: 3371 with ccp_alpha: 4.79438919481658e-05 and depth of 38 Number of nodes in the tree # 516 is: 3369 with ccp_alpha: 4.833304691527753e-05 and depth of 38 Number of nodes in the tree # 517 is: 3363 with ccp_alpha: 4.863904911163888e-05 and depth of 38 Number of nodes in the tree # 518 is: 3359 with ccp_alpha: 4.8759264260209464e-05 and depth of 38 Number of nodes in the tree # 519 is: 3347 with ccp_alpha: 4.8770897382883215e-05 and depth of 38 Number of nodes in the tree # 520 is: 3341 with ccp_alpha: 4.9009311769236165e-05 and depth of 38 Number of nodes in the tree # 521 is: 3325 with ccp_alpha: 4.9060505863787844e-05 and depth of 38 Number of nodes in the tree # 522 is: 3321 with ccp_alpha: 4.909855569926706e-05 and depth of 38 Number of nodes in the tree # 523 is: 3315 with ccp_alpha: 4.920394801604264e-05 and depth of 38 Number of nodes in the tree # 524 is: 3313 with ccp_alpha: 4.922810333963452e-05 and depth of 38 Number of nodes in the tree # 525 is: 3307 with ccp_alpha: 4.942077054341815e-05 and depth of 38 Number of nodes in the tree # 526 is: 3301 with ccp_alpha: 4.942115472528016e-05 and depth of 38 Number of nodes in the tree # 527 is: 3291 with ccp_alpha: 4.944016286171297e-05 and depth of 38 Number of nodes in the tree # 528 is: 3285 with ccp_alpha: 4.950940678728957e-05 and depth of 38 Number of nodes in the tree # 529 is: 3281 with ccp_alpha: 4.988447805082965e-05 and depth of 38 Number of nodes in the tree # 530 is: 3269 with ccp_alpha: 4.9947708971965636e-05 and depth of 38 Number of nodes in the tree # 531 is: 3263 with ccp_alpha: 4.9948514608019416e-05 and depth of 38 Number of nodes in the tree # 532 is: 3257 with ccp_alpha: 4.998545877562892e-05 and depth of 38 Number of nodes in the tree # 533 is: 3239 with ccp_alpha: 5.000950180534302e-05 and depth of 38 Number of nodes in the tree # 534 is: 3237 with ccp_alpha: 5.012315976399154e-05 and depth of 38 Number of nodes in the tree # 535 is: 3235 with ccp_alpha: 5.0349411124291825e-05 and depth of 38 Number of nodes in the tree # 536 is: 3229 with ccp_alpha: 5.0600523190315266e-05 and depth of 38 Number of nodes in the tree # 537 is: 3223 with ccp_alpha: 5.0634620577909796e-05 and depth of 38 Number of nodes in the tree # 538 is: 3223 with ccp_alpha: 5.0634620577909796e-05 and depth of 38 Number of nodes in the tree # 539 is: 3223 with ccp_alpha: 5.0634620577909796e-05 and depth of 38 Number of nodes in the tree # 540 is: 3219 with ccp_alpha: 5.063462057790981e-05 and depth of 38 Number of nodes in the tree # 541 is: 3217 with ccp_alpha: 5.0634620577909864e-05 and depth of 38 Number of nodes in the tree # 542 is: 3209 with ccp_alpha: 5.101821618834851e-05 and depth of 38 Number of nodes in the tree # 543 is: 3203 with ccp_alpha: 5.1384763104989946e-05 and depth of 38 Number of nodes in the tree # 544 is: 3195 with ccp_alpha: 5.1465744400527e-05 and depth of 38 Number of nodes in the tree # 545 is: 3195 with ccp_alpha: 5.1465744400527e-05 and depth of 38 Number of nodes in the tree # 546 is: 3181 with ccp_alpha: 5.156424276057007e-05 and depth of 38 Number of nodes in the tree # 547 is: 3173 with ccp_alpha: 5.171437118507061e-05 and depth of 38 Number of nodes in the tree # 548 is: 3165 with ccp_alpha: 5.198487712665407e-05 and depth of 38 Number of nodes in the tree # 549 is: 3151 with ccp_alpha: 5.209177897110556e-05 and depth of 38 Number of nodes in the tree # 550 is: 3143 with ccp_alpha: 5.2146692580783575e-05 and depth of 38 Number of nodes in the tree # 551 is: 3135 with ccp_alpha: 5.2461202507508644e-05 and depth of 38 Number of nodes in the tree # 552 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 553 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 554 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 555 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 556 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 557 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 558 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 559 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 560 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 561 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 562 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 563 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 564 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 565 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 566 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 567 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 568 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 569 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 570 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 571 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 572 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 573 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 574 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 575 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 576 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 577 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 578 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 579 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 580 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 581 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 582 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 583 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 584 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 585 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 586 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 587 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 588 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 589 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 590 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 591 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 592 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 593 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 594 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 595 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 596 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 597 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 598 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 599 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 600 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 601 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 602 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 603 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 604 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 605 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 606 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 607 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 608 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 609 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 610 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 611 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 612 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 613 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 614 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 615 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 616 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 617 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 618 is: 2957 with ccp_alpha: 5.250997689561016e-05 and depth of 38 Number of nodes in the tree # 619 is: 2955 with ccp_alpha: 5.2509976895610176e-05 and depth of 38 Number of nodes in the tree # 620 is: 2945 with ccp_alpha: 5.278442291952801e-05 and depth of 38 Number of nodes in the tree # 621 is: 2933 with ccp_alpha: 5.2894261645579116e-05 and depth of 38 Number of nodes in the tree # 622 is: 2929 with ccp_alpha: 5.2987340321933895e-05 and depth of 38 Number of nodes in the tree # 623 is: 2921 with ccp_alpha: 5.3106681178514815e-05 and depth of 38 Number of nodes in the tree # 624 is: 2913 with ccp_alpha: 5.316635160680529e-05 and depth of 38 Number of nodes in the tree # 625 is: 2913 with ccp_alpha: 5.316635160680529e-05 and depth of 38 Number of nodes in the tree # 626 is: 2901 with ccp_alpha: 5.31663516068053e-05 and depth of 38 Number of nodes in the tree # 627 is: 2901 with ccp_alpha: 5.31663516068053e-05 and depth of 38 Number of nodes in the tree # 628 is: 2901 with ccp_alpha: 5.31663516068053e-05 and depth of 38 Number of nodes in the tree # 629 is: 2901 with ccp_alpha: 5.31663516068053e-05 and depth of 38 Number of nodes in the tree # 630 is: 2901 with ccp_alpha: 5.31663516068053e-05 and depth of 38 Number of nodes in the tree # 631 is: 2897 with ccp_alpha: 5.344765505446036e-05 and depth of 38 Number of nodes in the tree # 632 is: 2895 with ccp_alpha: 5.360393474760204e-05 and depth of 38 Number of nodes in the tree # 633 is: 2893 with ccp_alpha: 5.3994298583960045e-05 and depth of 38 Number of nodes in the tree # 634 is: 2891 with ccp_alpha: 5.4010261949770394e-05 and depth of 38 Number of nodes in the tree # 635 is: 2887 with ccp_alpha: 5.4383044199496464e-05 and depth of 38 Number of nodes in the tree # 636 is: 2885 with ccp_alpha: 5.469789259959388e-05 and depth of 38 Number of nodes in the tree # 637 is: 2883 with ccp_alpha: 5.4854172292735675e-05 and depth of 38 Number of nodes in the tree # 638 is: 2873 with ccp_alpha: 5.486664475819785e-05 and depth of 38 Number of nodes in the tree # 639 is: 2869 with ccp_alpha: 5.4940889896899875e-05 and depth of 38 Number of nodes in the tree # 640 is: 2865 with ccp_alpha: 5.508039534504563e-05 and depth of 38 Number of nodes in the tree # 641 is: 2845 with ccp_alpha: 5.509408682811025e-05 and depth of 38 Number of nodes in the tree # 642 is: 2821 with ccp_alpha: 5.513547574039068e-05 and depth of 38 Number of nodes in the tree # 643 is: 2821 with ccp_alpha: 5.513547574039068e-05 and depth of 38 Number of nodes in the tree # 644 is: 2821 with ccp_alpha: 5.513547574039068e-05 and depth of 38 Number of nodes in the tree # 645 is: 2821 with ccp_alpha: 5.513547574039068e-05 and depth of 38 Number of nodes in the tree # 646 is: 2801 with ccp_alpha: 5.5228019240962157e-05 and depth of 38 Number of nodes in the tree # 647 is: 2797 with ccp_alpha: 5.5264723270394387e-05 and depth of 38 Number of nodes in the tree # 648 is: 2793 with ccp_alpha: 5.54271978342552e-05 and depth of 38 Number of nodes in the tree # 649 is: 2787 with ccp_alpha: 5.549834956446604e-05 and depth of 38 Number of nodes in the tree # 650 is: 2773 with ccp_alpha: 5.572487344023936e-05 and depth of 38 Number of nodes in the tree # 651 is: 2767 with ccp_alpha: 5.5788815511769275e-05 and depth of 38 Number of nodes in the tree # 652 is: 2765 with ccp_alpha: 5.589771734048826e-05 and depth of 38 Number of nodes in the tree # 653 is: 2763 with ccp_alpha: 5.601064202198415e-05 and depth of 38 Number of nodes in the tree # 654 is: 2759 with ccp_alpha: 5.601064202198419e-05 and depth of 38 Number of nodes in the tree # 655 is: 2753 with ccp_alpha: 5.6062122391489684e-05 and depth of 38 Number of nodes in the tree # 656 is: 2745 with ccp_alpha: 5.6260689531010896e-05 and depth of 38 Number of nodes in the tree # 657 is: 2745 with ccp_alpha: 5.6260689531010896e-05 and depth of 38 Number of nodes in the tree # 658 is: 2737 with ccp_alpha: 5.630572275837673e-05 and depth of 38 Number of nodes in the tree # 659 is: 2731 with ccp_alpha: 5.64412051123938e-05 and depth of 38 Number of nodes in the tree # 660 is: 2727 with ccp_alpha: 5.6465273856578184e-05 and depth of 38 Number of nodes in the tree # 661 is: 2721 with ccp_alpha: 5.650267099135935e-05 and depth of 38 Number of nodes in the tree # 662 is: 2715 with ccp_alpha: 5.688580830357769e-05 and depth of 38 Number of nodes in the tree # 663 is: 2705 with ccp_alpha: 5.702572085483122e-05 and depth of 38 Number of nodes in the tree # 664 is: 2703 with ccp_alpha: 5.705309360891223e-05 and depth of 38 Number of nodes in the tree # 665 is: 2697 with ccp_alpha: 5.728361115884745e-05 and depth of 38 Number of nodes in the tree # 666 is: 2691 with ccp_alpha: 5.747016698062912e-05 and depth of 38 Number of nodes in the tree # 667 is: 2685 with ccp_alpha: 5.751092707614448e-05 and depth of 38 Number of nodes in the tree # 668 is: 2679 with ccp_alpha: 5.776097458517115e-05 and depth of 38 Number of nodes in the tree # 669 is: 2671 with ccp_alpha: 5.776097458517118e-05 and depth of 38 Number of nodes in the tree # 670 is: 2671 with ccp_alpha: 5.776097458517118e-05 and depth of 38 Number of nodes in the tree # 671 is: 2667 with ccp_alpha: 5.7979766155569565e-05 and depth of 38 Number of nodes in the tree # 672 is: 2659 with ccp_alpha: 5.799965629833305e-05 and depth of 38 Number of nodes in the tree # 673 is: 2659 with ccp_alpha: 5.799965629833305e-05 and depth of 38 Number of nodes in the tree # 674 is: 2659 with ccp_alpha: 5.799965629833305e-05 and depth of 38 Number of nodes in the tree # 675 is: 2659 with ccp_alpha: 5.799965629833305e-05 and depth of 38 Number of nodes in the tree # 676 is: 2657 with ccp_alpha: 5.8366858933966666e-05 and depth of 38 Number of nodes in the tree # 677 is: 2641 with ccp_alpha: 5.845231014151308e-05 and depth of 38 Number of nodes in the tree # 678 is: 2631 with ccp_alpha: 5.859007948352293e-05 and depth of 38 Number of nodes in the tree # 679 is: 2629 with ccp_alpha: 5.862500997606086e-05 and depth of 38 Number of nodes in the tree # 680 is: 2617 with ccp_alpha: 5.890786767863616e-05 and depth of 38 Number of nodes in the tree # 681 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 682 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 683 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 684 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 685 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 686 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 687 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 688 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 689 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 690 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 691 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 692 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 693 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 694 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 695 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 696 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 697 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 698 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 699 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 700 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 701 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 702 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 703 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 704 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 705 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 706 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 707 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 708 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 709 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 710 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 711 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 712 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 713 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 714 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 715 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 716 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 717 is: 2521 with ccp_alpha: 5.907372400756144e-05 and depth of 38 Number of nodes in the tree # 718 is: 2519 with ccp_alpha: 5.9073724007561445e-05 and depth of 38 Number of nodes in the tree # 719 is: 2513 with ccp_alpha: 5.9242025215560176e-05 and depth of 38 Number of nodes in the tree # 720 is: 2503 with ccp_alpha: 5.9332088104986225e-05 and depth of 38 Number of nodes in the tree # 721 is: 2497 with ccp_alpha: 5.9432344777086576e-05 and depth of 38 Number of nodes in the tree # 722 is: 2487 with ccp_alpha: 5.96704282904661e-05 and depth of 38 Number of nodes in the tree # 723 is: 2487 with ccp_alpha: 5.96704282904661e-05 and depth of 38 Number of nodes in the tree # 724 is: 2487 with ccp_alpha: 5.96704282904661e-05 and depth of 38 Number of nodes in the tree # 725 is: 2487 with ccp_alpha: 5.96704282904661e-05 and depth of 38 Number of nodes in the tree # 726 is: 2481 with ccp_alpha: 5.9721242088470574e-05 and depth of 38 Number of nodes in the tree # 727 is: 2475 with ccp_alpha: 5.980302924222269e-05 and depth of 38 Number of nodes in the tree # 728 is: 2471 with ccp_alpha: 5.989419239655534e-05 and depth of 38 Number of nodes in the tree # 729 is: 2451 with ccp_alpha: 5.995568904362391e-05 and depth of 38 Number of nodes in the tree # 730 is: 2429 with ccp_alpha: 6.001140216641163e-05 and depth of 38 Number of nodes in the tree # 731 is: 2429 with ccp_alpha: 6.001140216641163e-05 and depth of 38 Number of nodes in the tree # 732 is: 2429 with ccp_alpha: 6.001140216641163e-05 and depth of 38 Number of nodes in the tree # 733 is: 2429 with ccp_alpha: 6.001140216641163e-05 and depth of 38 Number of nodes in the tree # 734 is: 2429 with ccp_alpha: 6.001140216641163e-05 and depth of 38 Number of nodes in the tree # 735 is: 2429 with ccp_alpha: 6.001140216641163e-05 and depth of 38 Number of nodes in the tree # 736 is: 2429 with ccp_alpha: 6.001140216641163e-05 and depth of 38 Number of nodes in the tree # 737 is: 2429 with ccp_alpha: 6.001140216641163e-05 and depth of 38 Number of nodes in the tree # 738 is: 2429 with ccp_alpha: 6.001140216641163e-05 and depth of 38 Number of nodes in the tree # 739 is: 2429 with ccp_alpha: 6.001140216641163e-05 and depth of 38 Number of nodes in the tree # 740 is: 2417 with ccp_alpha: 6.0167681859553305e-05 and depth of 38 Number of nodes in the tree # 741 is: 2413 with ccp_alpha: 6.054091453846819e-05 and depth of 38 Number of nodes in the tree # 742 is: 2407 with ccp_alpha: 6.061813509272644e-05 and depth of 38 Number of nodes in the tree # 743 is: 2397 with ccp_alpha: 6.0724037567137765e-05 and depth of 38 Number of nodes in the tree # 744 is: 2379 with ccp_alpha: 6.0853228780134894e-05 and depth of 38 Number of nodes in the tree # 745 is: 2375 with ccp_alpha: 6.0918131090759154e-05 and depth of 38 Number of nodes in the tree # 746 is: 2367 with ccp_alpha: 6.106202792166875e-05 and depth of 38 Number of nodes in the tree # 747 is: 2363 with ccp_alpha: 6.109333850354643e-05 and depth of 38 Number of nodes in the tree # 748 is: 2363 with ccp_alpha: 6.109333850354643e-05 and depth of 38 Number of nodes in the tree # 749 is: 2361 with ccp_alpha: 6.110251856943734e-05 and depth of 38 Number of nodes in the tree # 750 is: 2351 with ccp_alpha: 6.11991278342885e-05 and depth of 38 Number of nodes in the tree # 751 is: 2339 with ccp_alpha: 6.126163971154519e-05 and depth of 38 Number of nodes in the tree # 752 is: 2339 with ccp_alpha: 6.126163971154519e-05 and depth of 38 Number of nodes in the tree # 753 is: 2339 with ccp_alpha: 6.126163971154519e-05 and depth of 38 Number of nodes in the tree # 754 is: 2335 with ccp_alpha: 6.139034063530895e-05 and depth of 38 Number of nodes in the tree # 755 is: 2331 with ccp_alpha: 6.14366729678639e-05 and depth of 38 Number of nodes in the tree # 756 is: 2329 with ccp_alpha: 6.153512917454315e-05 and depth of 38 Number of nodes in the tree # 757 is: 2327 with ccp_alpha: 6.15457846081665e-05 and depth of 38 Number of nodes in the tree # 758 is: 2315 with ccp_alpha: 6.156794791010292e-05 and depth of 38 Number of nodes in the tree # 759 is: 2297 with ccp_alpha: 6.162727662165497e-05 and depth of 38 Number of nodes in the tree # 760 is: 2291 with ccp_alpha: 6.180861863754115e-05 and depth of 38 Number of nodes in the tree # 761 is: 2289 with ccp_alpha: 6.188675848411198e-05 and depth of 38 Number of nodes in the tree # 762 is: 2281 with ccp_alpha: 6.218543048848731e-05 and depth of 38 Number of nodes in the tree # 763 is: 2271 with ccp_alpha: 6.218576634455651e-05 and depth of 38 Number of nodes in the tree # 764 is: 2269 with ccp_alpha: 6.226479473787758e-05 and depth of 38 Number of nodes in the tree # 765 is: 2267 with ccp_alpha: 6.231953301896596e-05 and depth of 38 Number of nodes in the tree # 766 is: 2261 with ccp_alpha: 6.235559756353707e-05 and depth of 38 Number of nodes in the tree # 767 is: 2257 with ccp_alpha: 6.242672175824863e-05 and depth of 38 Number of nodes in the tree # 768 is: 2255 with ccp_alpha: 6.251187725667877e-05 and depth of 38 Number of nodes in the tree # 769 is: 2245 with ccp_alpha: 6.260445006257216e-05 and depth of 38 Number of nodes in the tree # 770 is: 2243 with ccp_alpha: 6.261860485199522e-05 and depth of 38 Number of nodes in the tree # 771 is: 2237 with ccp_alpha: 6.265706613288787e-05 and depth of 38 Number of nodes in the tree # 772 is: 2231 with ccp_alpha: 6.301153352158296e-05 and depth of 38 Number of nodes in the tree # 773 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 774 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 775 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 776 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 777 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 778 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 779 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 780 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 781 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 782 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 783 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 784 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 785 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 786 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 787 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 788 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 789 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 790 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 791 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 792 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 793 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 794 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 795 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 796 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 797 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 798 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 799 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 800 is: 2165 with ccp_alpha: 6.301197227473218e-05 and depth of 38 Number of nodes in the tree # 801 is: 2161 with ccp_alpha: 6.30119722747322e-05 and depth of 38 Number of nodes in the tree # 802 is: 2159 with ccp_alpha: 6.313200596647485e-05 and depth of 38 Number of nodes in the tree # 803 is: 2155 with ccp_alpha: 6.329327572238728e-05 and depth of 38 Number of nodes in the tree # 804 is: 2151 with ccp_alpha: 6.338704353827228e-05 and depth of 38 Number of nodes in the tree # 805 is: 2151 with ccp_alpha: 6.338704353827228e-05 and depth of 38 Number of nodes in the tree # 806 is: 2147 with ccp_alpha: 6.344955541552895e-05 and depth of 38 Number of nodes in the tree # 807 is: 2145 with ccp_alpha: 6.360767369329579e-05 and depth of 38 Number of nodes in the tree # 808 is: 2143 with ccp_alpha: 6.361785662352771e-05 and depth of 38 Number of nodes in the tree # 809 is: 2139 with ccp_alpha: 6.36683469859273e-05 and depth of 38 Number of nodes in the tree # 810 is: 2135 with ccp_alpha: 6.37532895956114e-05 and depth of 38 Number of nodes in the tree # 811 is: 2133 with ccp_alpha: 6.376211480181235e-05 and depth of 38 Number of nodes in the tree # 812 is: 2131 with ccp_alpha: 6.392518926422115e-05 and depth of 38 Number of nodes in the tree # 813 is: 2127 with ccp_alpha: 6.394243752466814e-05 and depth of 38 Number of nodes in the tree # 814 is: 2121 with ccp_alpha: 6.399653434152489e-05 and depth of 38 Number of nodes in the tree # 815 is: 2115 with ccp_alpha: 6.405547561759085e-05 and depth of 38 Number of nodes in the tree # 816 is: 2111 with ccp_alpha: 6.420337511185951e-05 and depth of 38 Number of nodes in the tree # 817 is: 2109 with ccp_alpha: 6.42105695734363e-05 and depth of 38 Number of nodes in the tree # 818 is: 2107 with ccp_alpha: 6.462766387152019e-05 and depth of 38 Number of nodes in the tree # 819 is: 2099 with ccp_alpha: 6.462766387152022e-05 and depth of 38 Number of nodes in the tree # 820 is: 2099 with ccp_alpha: 6.462766387152022e-05 and depth of 38 Number of nodes in the tree # 821 is: 2093 with ccp_alpha: 6.469979296066252e-05 and depth of 38 Number of nodes in the tree # 822 is: 2085 with ccp_alpha: 6.469979296066253e-05 and depth of 38 Number of nodes in the tree # 823 is: 2079 with ccp_alpha: 6.502366671387015e-05 and depth of 38 Number of nodes in the tree # 824 is: 2077 with ccp_alpha: 6.510599362056916e-05 and depth of 38 Number of nodes in the tree # 825 is: 2075 with ccp_alpha: 6.51165388090404e-05 and depth of 38 Number of nodes in the tree # 826 is: 2073 with ccp_alpha: 6.515484265539863e-05 and depth of 38 Number of nodes in the tree # 827 is: 2067 with ccp_alpha: 6.540716420330391e-05 and depth of 38 Number of nodes in the tree # 828 is: 2063 with ccp_alpha: 6.55167585289481e-05 and depth of 38 Number of nodes in the tree # 829 is: 2057 with ccp_alpha: 6.556973379121596e-05 and depth of 38 Number of nodes in the tree # 830 is: 2055 with ccp_alpha: 6.557280366028162e-05 and depth of 38 Number of nodes in the tree # 831 is: 2049 with ccp_alpha: 6.56374711195127e-05 and depth of 38 Number of nodes in the tree # 832 is: 2011 with ccp_alpha: 6.563747111951271e-05 and depth of 38 Number of nodes in the tree # 833 is: 2011 with ccp_alpha: 6.563747111951271e-05 and depth of 38 Number of nodes in the tree # 834 is: 2011 with ccp_alpha: 6.563747111951271e-05 and depth of 38 Number of nodes in the tree # 835 is: 2011 with ccp_alpha: 6.563747111951271e-05 and depth of 38 Number of nodes in the tree # 836 is: 2011 with ccp_alpha: 6.563747111951271e-05 and depth of 38 Number of nodes in the tree # 837 is: 2011 with ccp_alpha: 6.563747111951271e-05 and depth of 38 Number of nodes in the tree # 838 is: 2011 with ccp_alpha: 6.563747111951271e-05 and depth of 38 Number of nodes in the tree # 839 is: 2011 with ccp_alpha: 6.563747111951271e-05 and depth of 38 Number of nodes in the tree # 840 is: 2011 with ccp_alpha: 6.563747111951271e-05 and depth of 38 Number of nodes in the tree # 841 is: 2011 with ccp_alpha: 6.563747111951271e-05 and depth of 38 Number of nodes in the tree # 842 is: 2011 with ccp_alpha: 6.563747111951271e-05 and depth of 38 Number of nodes in the tree # 843 is: 2011 with ccp_alpha: 6.563747111951271e-05 and depth of 38 Number of nodes in the tree # 844 is: 2011 with ccp_alpha: 6.563747111951271e-05 and depth of 38 Number of nodes in the tree # 845 is: 2011 with ccp_alpha: 6.563747111951271e-05 and depth of 38 Number of nodes in the tree # 846 is: 2011 with ccp_alpha: 6.563747111951271e-05 and depth of 38 Number of nodes in the tree # 847 is: 2011 with ccp_alpha: 6.563747111951271e-05 and depth of 38 Number of nodes in the tree # 848 is: 2011 with ccp_alpha: 6.563747111951271e-05 and depth of 38 Number of nodes in the tree # 849 is: 2007 with ccp_alpha: 6.587189065922525e-05 and depth of 38 Number of nodes in the tree # 850 is: 2005 with ccp_alpha: 6.601254238305281e-05 and depth of 38 Number of nodes in the tree # 851 is: 2001 with ccp_alpha: 6.623417540241739e-05 and depth of 38 Number of nodes in the tree # 852 is: 1999 with ccp_alpha: 6.655869878434794e-05 and depth of 38 Number of nodes in the tree # 853 is: 1995 with ccp_alpha: 6.662203318630542e-05 and depth of 38 Number of nodes in the tree # 854 is: 1981 with ccp_alpha: 6.703677240097848e-05 and depth of 38 Number of nodes in the tree # 855 is: 1977 with ccp_alpha: 6.715749676649089e-05 and depth of 38 Number of nodes in the tree # 856 is: 1977 with ccp_alpha: 6.715749676649089e-05 and depth of 38 Number of nodes in the tree # 857 is: 1965 with ccp_alpha: 6.723039541889118e-05 and depth of 38 Number of nodes in the tree # 858 is: 1961 with ccp_alpha: 6.724128263103018e-05 and depth of 38 Number of nodes in the tree # 859 is: 1959 with ccp_alpha: 6.739248371629108e-05 and depth of 38 Number of nodes in the tree # 860 is: 1955 with ccp_alpha: 6.742758396822667e-05 and depth of 38 Number of nodes in the tree # 861 is: 1945 with ccp_alpha: 6.745512416589923e-05 and depth of 38 Number of nodes in the tree # 862 is: 1927 with ccp_alpha: 6.751282743721308e-05 and depth of 38 Number of nodes in the tree # 863 is: 1927 with ccp_alpha: 6.751282743721308e-05 and depth of 38 Number of nodes in the tree # 864 is: 1927 with ccp_alpha: 6.751282743721308e-05 and depth of 38 Number of nodes in the tree # 865 is: 1927 with ccp_alpha: 6.751282743721308e-05 and depth of 38 Number of nodes in the tree # 866 is: 1927 with ccp_alpha: 6.751282743721308e-05 and depth of 38 Number of nodes in the tree # 867 is: 1927 with ccp_alpha: 6.751282743721308e-05 and depth of 38 Number of nodes in the tree # 868 is: 1927 with ccp_alpha: 6.751282743721308e-05 and depth of 38 Number of nodes in the tree # 869 is: 1927 with ccp_alpha: 6.751282743721308e-05 and depth of 38 Number of nodes in the tree # 870 is: 1923 with ccp_alpha: 6.763320503868706e-05 and depth of 38 Number of nodes in the tree # 871 is: 1921 with ccp_alpha: 6.770036306898313e-05 and depth of 38 Number of nodes in the tree # 872 is: 1919 with ccp_alpha: 6.770036306898315e-05 and depth of 38 Number of nodes in the tree # 873 is: 1911 with ccp_alpha: 6.783563166377207e-05 and depth of 38 Number of nodes in the tree # 874 is: 1903 with ccp_alpha: 6.798166651663816e-05 and depth of 38 Number of nodes in the tree # 875 is: 1899 with ccp_alpha: 6.806299339380626e-05 and depth of 38 Number of nodes in the tree # 876 is: 1879 with ccp_alpha: 6.815432863278511e-05 and depth of 38 Number of nodes in the tree # 877 is: 1875 with ccp_alpha: 6.816198923949399e-05 and depth of 38 Number of nodes in the tree # 878 is: 1873 with ccp_alpha: 6.819201053605582e-05 and depth of 38 Number of nodes in the tree # 879 is: 1855 with ccp_alpha: 6.826296996429323e-05 and depth of 38 Number of nodes in the tree # 880 is: 1855 with ccp_alpha: 6.826296996429323e-05 and depth of 38 Number of nodes in the tree # 881 is: 1855 with ccp_alpha: 6.826296996429323e-05 and depth of 38 Number of nodes in the tree # 882 is: 1845 with ccp_alpha: 6.841299846970926e-05 and depth of 38 Number of nodes in the tree # 883 is: 1841 with ccp_alpha: 6.849804369807013e-05 and depth of 38 Number of nodes in the tree # 884 is: 1837 with ccp_alpha: 6.862099253403595e-05 and depth of 38 Number of nodes in the tree # 885 is: 1825 with ccp_alpha: 6.884141776723925e-05 and depth of 38 Number of nodes in the tree # 886 is: 1823 with ccp_alpha: 6.891251218369972e-05 and depth of 38 Number of nodes in the tree # 887 is: 1809 with ccp_alpha: 6.891934467548834e-05 and depth of 38 Number of nodes in the tree # 888 is: 1809 with ccp_alpha: 6.891934467548834e-05 and depth of 38 Number of nodes in the tree # 889 is: 1809 with ccp_alpha: 6.891934467548834e-05 and depth of 38 Number of nodes in the tree # 890 is: 1809 with ccp_alpha: 6.891934467548834e-05 and depth of 38 Number of nodes in the tree # 891 is: 1809 with ccp_alpha: 6.891934467548834e-05 and depth of 38 Number of nodes in the tree # 892 is: 1809 with ccp_alpha: 6.891934467548834e-05 and depth of 38 Number of nodes in the tree # 893 is: 1809 with ccp_alpha: 6.891934467548834e-05 and depth of 38 Number of nodes in the tree # 894 is: 1807 with ccp_alpha: 6.906203483009597e-05 and depth of 38 Number of nodes in the tree # 895 is: 1805 with ccp_alpha: 6.928664580968307e-05 and depth of 38 Number of nodes in the tree # 896 is: 1801 with ccp_alpha: 6.94116257088847e-05 and depth of 38 Number of nodes in the tree # 897 is: 1797 with ccp_alpha: 6.94331311016406e-05 and depth of 38 Number of nodes in the tree # 898 is: 1795 with ccp_alpha: 6.944444444444439e-05 and depth of 38 Number of nodes in the tree # 899 is: 1791 with ccp_alpha: 6.949849883242521e-05 and depth of 38 Number of nodes in the tree # 900 is: 1789 with ccp_alpha: 6.979787698124179e-05 and depth of 38 Number of nodes in the tree # 901 is: 1785 with ccp_alpha: 6.996357717057153e-05 and depth of 38 Number of nodes in the tree # 902 is: 1769 with ccp_alpha: 7.001330252748022e-05 and depth of 37 Number of nodes in the tree # 903 is: 1769 with ccp_alpha: 7.001330252748022e-05 and depth of 37 Number of nodes in the tree # 904 is: 1769 with ccp_alpha: 7.001330252748022e-05 and depth of 37 Number of nodes in the tree # 905 is: 1769 with ccp_alpha: 7.001330252748022e-05 and depth of 37 Number of nodes in the tree # 906 is: 1769 with ccp_alpha: 7.001330252748022e-05 and depth of 37 Number of nodes in the tree # 907 is: 1769 with ccp_alpha: 7.001330252748022e-05 and depth of 37 Number of nodes in the tree # 908 is: 1769 with ccp_alpha: 7.001330252748022e-05 and depth of 37 Number of nodes in the tree # 909 is: 1769 with ccp_alpha: 7.001330252748022e-05 and depth of 37 Number of nodes in the tree # 910 is: 1767 with ccp_alpha: 7.001330252748024e-05 and depth of 37 Number of nodes in the tree # 911 is: 1755 with ccp_alpha: 7.017296071686891e-05 and depth of 37 Number of nodes in the tree # 912 is: 1751 with ccp_alpha: 7.047391635989785e-05 and depth of 37 Number of nodes in the tree # 913 is: 1745 with ccp_alpha: 7.058632806899979e-05 and depth of 37 Number of nodes in the tree # 914 is: 1743 with ccp_alpha: 7.064402581318039e-05 and depth of 37 Number of nodes in the tree # 915 is: 1733 with ccp_alpha: 7.064463608417497e-05 and depth of 37 Number of nodes in the tree # 916 is: 1729 with ccp_alpha: 7.070160615205894e-05 and depth of 37 Number of nodes in the tree # 917 is: 1727 with ccp_alpha: 7.071386666914986e-05 and depth of 37 Number of nodes in the tree # 918 is: 1719 with ccp_alpha: 7.080890823801976e-05 and depth of 37 Number of nodes in the tree # 919 is: 1717 with ccp_alpha: 7.08412459414723e-05 and depth of 37 Number of nodes in the tree # 920 is: 1713 with ccp_alpha: 7.08884688090737e-05 and depth of 37 Number of nodes in the tree # 921 is: 1713 with ccp_alpha: 7.08884688090737e-05 and depth of 37 Number of nodes in the tree # 922 is: 1703 with ccp_alpha: 7.088846880907374e-05 and depth of 37 Number of nodes in the tree # 923 is: 1703 with ccp_alpha: 7.088846880907374e-05 and depth of 37 Number of nodes in the tree # 924 is: 1703 with ccp_alpha: 7.088846880907374e-05 and depth of 37 Number of nodes in the tree # 925 is: 1703 with ccp_alpha: 7.088846880907374e-05 and depth of 37 Number of nodes in the tree # 926 is: 1697 with ccp_alpha: 7.091600900674623e-05 and depth of 37 Number of nodes in the tree # 927 is: 1681 with ccp_alpha: 7.126458839630247e-05 and depth of 37 Number of nodes in the tree # 928 is: 1679 with ccp_alpha: 7.146015000914695e-05 and depth of 37 Number of nodes in the tree # 929 is: 1669 with ccp_alpha: 7.160451394855933e-05 and depth of 37 Number of nodes in the tree # 930 is: 1669 with ccp_alpha: 7.160451394855933e-05 and depth of 37 Number of nodes in the tree # 931 is: 1669 with ccp_alpha: 7.160451394855933e-05 and depth of 37 Number of nodes in the tree # 932 is: 1669 with ccp_alpha: 7.160451394855933e-05 and depth of 37 Number of nodes in the tree # 933 is: 1665 with ccp_alpha: 7.170893719806763e-05 and depth of 37 Number of nodes in the tree # 934 is: 1659 with ccp_alpha: 7.174024269685228e-05 and depth of 37 Number of nodes in the tree # 935 is: 1657 with ccp_alpha: 7.201368259969395e-05 and depth of 37 Number of nodes in the tree # 936 is: 1655 with ccp_alpha: 7.220121823146398e-05 and depth of 37 Number of nodes in the tree # 937 is: 1653 with ccp_alpha: 7.260697714383911e-05 and depth of 37 Number of nodes in the tree # 938 is: 1649 with ccp_alpha: 7.269000791001353e-05 and depth of 37 Number of nodes in the tree # 939 is: 1641 with ccp_alpha: 7.27061218554602e-05 and depth of 37 Number of nodes in the tree # 940 is: 1641 with ccp_alpha: 7.27061218554602e-05 and depth of 37 Number of nodes in the tree # 941 is: 1641 with ccp_alpha: 7.27061218554602e-05 and depth of 37 Number of nodes in the tree # 942 is: 1633 with ccp_alpha: 7.278037238840085e-05 and depth of 37 Number of nodes in the tree # 943 is: 1613 with ccp_alpha: 7.296772508176741e-05 and depth of 37 Number of nodes in the tree # 944 is: 1609 with ccp_alpha: 7.313889639031418e-05 and depth of 37 Number of nodes in the tree # 945 is: 1609 with ccp_alpha: 7.313889639031418e-05 and depth of 37 Number of nodes in the tree # 946 is: 1605 with ccp_alpha: 7.32000493137174e-05 and depth of 37 Number of nodes in the tree # 947 is: 1599 with ccp_alpha: 7.33034013304633e-05 and depth of 37 Number of nodes in the tree # 948 is: 1595 with ccp_alpha: 7.35139676538542e-05 and depth of 37 Number of nodes in the tree # 949 is: 1595 with ccp_alpha: 7.35139676538542e-05 and depth of 37 Number of nodes in the tree # 950 is: 1591 with ccp_alpha: 7.351396765385425e-05 and depth of 37 Number of nodes in the tree # 951 is: 1591 with ccp_alpha: 7.351396765385425e-05 and depth of 37 Number of nodes in the tree # 952 is: 1583 with ccp_alpha: 7.354979187297492e-05 and depth of 37 Number of nodes in the tree # 953 is: 1579 with ccp_alpha: 7.38421550094518e-05 and depth of 37 Number of nodes in the tree # 954 is: 1577 with ccp_alpha: 7.386047357928844e-05 and depth of 37 Number of nodes in the tree # 955 is: 1573 with ccp_alpha: 7.399043242914985e-05 and depth of 37 Number of nodes in the tree # 956 is: 1571 with ccp_alpha: 7.400444546002205e-05 and depth of 37 Number of nodes in the tree # 957 is: 1567 with ccp_alpha: 7.41597425249926e-05 and depth of 37 Number of nodes in the tree # 958 is: 1561 with ccp_alpha: 7.430657107869366e-05 and depth of 37 Number of nodes in the tree # 959 is: 1559 with ccp_alpha: 7.438913393544776e-05 and depth of 37 Number of nodes in the tree # 960 is: 1555 with ccp_alpha: 7.446427362198241e-05 and depth of 37 Number of nodes in the tree # 961 is: 1553 with ccp_alpha: 7.448125670193146e-05 and depth of 37 Number of nodes in the tree # 962 is: 1527 with ccp_alpha: 7.46117511739971e-05 and depth of 37 Number of nodes in the tree # 963 is: 1525 with ccp_alpha: 7.46194408516566e-05 and depth of 37 Number of nodes in the tree # 964 is: 1515 with ccp_alpha: 7.487274854949715e-05 and depth of 37 Number of nodes in the tree # 965 is: 1513 with ccp_alpha: 7.518473964598728e-05 and depth of 37 Number of nodes in the tree # 966 is: 1511 with ccp_alpha: 7.518473964598732e-05 and depth of 37 Number of nodes in the tree # 967 is: 1507 with ccp_alpha: 7.523767756547749e-05 and depth of 37 Number of nodes in the tree # 968 is: 1501 with ccp_alpha: 7.526051161841957e-05 and depth of 37 Number of nodes in the tree # 969 is: 1499 with ccp_alpha: 7.5274245288423e-05 and depth of 37 Number of nodes in the tree # 970 is: 1489 with ccp_alpha: 7.53703354705928e-05 and depth of 37 Number of nodes in the tree # 971 is: 1487 with ccp_alpha: 7.560961563027414e-05 and depth of 37 Number of nodes in the tree # 972 is: 1485 with ccp_alpha: 7.561751418084161e-05 and depth of 37 Number of nodes in the tree # 973 is: 1483 with ccp_alpha: 7.595193086686465e-05 and depth of 37 Number of nodes in the tree # 974 is: 1481 with ccp_alpha: 7.59519308668647e-05 and depth of 37 Number of nodes in the tree # 975 is: 1479 with ccp_alpha: 7.609419927717318e-05 and depth of 37 Number of nodes in the tree # 976 is: 1471 with ccp_alpha: 7.617039878109373e-05 and depth of 37 Number of nodes in the tree # 977 is: 1469 with ccp_alpha: 7.637814821179656e-05 and depth of 37 Number of nodes in the tree # 978 is: 1465 with ccp_alpha: 7.637814821179663e-05 and depth of 37 Number of nodes in the tree # 979 is: 1465 with ccp_alpha: 7.637814821179663e-05 and depth of 37 Number of nodes in the tree # 980 is: 1463 with ccp_alpha: 7.64099724402184e-05 and depth of 37 Number of nodes in the tree # 981 is: 1459 with ccp_alpha: 7.657704963943149e-05 and depth of 37 Number of nodes in the tree # 982 is: 1453 with ccp_alpha: 7.659846979317679e-05 and depth of 37 Number of nodes in the tree # 983 is: 1451 with ccp_alpha: 7.671912208774212e-05 and depth of 37 Number of nodes in the tree # 984 is: 1449 with ccp_alpha: 7.674535084743018e-05 and depth of 37 Number of nodes in the tree # 985 is: 1447 with ccp_alpha: 7.67958412098298e-05 and depth of 37 Number of nodes in the tree # 986 is: 1443 with ccp_alpha: 7.697485249470124e-05 and depth of 37 Number of nodes in the tree # 987 is: 1435 with ccp_alpha: 7.701463278022823e-05 and depth of 33 Number of nodes in the tree # 988 is: 1433 with ccp_alpha: 7.727883392184129e-05 and depth of 33 Number of nodes in the tree # 989 is: 1431 with ccp_alpha: 7.730635487409267e-05 and depth of 33 Number of nodes in the tree # 990 is: 1429 with ccp_alpha: 7.733287506444405e-05 and depth of 33 Number of nodes in the tree # 991 is: 1427 with ccp_alpha: 7.784645332867626e-05 and depth of 33 Number of nodes in the tree # 992 is: 1425 with ccp_alpha: 7.789941627370743e-05 and depth of 33 Number of nodes in the tree # 993 is: 1423 with ccp_alpha: 7.79368859304048e-05 and depth of 33 Number of nodes in the tree # 994 is: 1417 with ccp_alpha: 7.794861429673398e-05 and depth of 33 Number of nodes in the tree # 995 is: 1413 with ccp_alpha: 7.812563932601735e-05 and depth of 33 Number of nodes in the tree # 996 is: 1411 with ccp_alpha: 7.832979978903182e-05 and depth of 33 Number of nodes in the tree # 997 is: 1403 with ccp_alpha: 7.861578927268907e-05 and depth of 33 Number of nodes in the tree # 998 is: 1399 with ccp_alpha: 7.862822061191625e-05 and depth of 33 Number of nodes in the tree # 999 is: 1395 with ccp_alpha: 7.876496534341525e-05 and depth of 33 Number of nodes in the tree # 1000 is: 1395 with ccp_alpha: 7.876496534341525e-05 and depth of 33 Number of nodes in the tree # 1001 is: 1391 with ccp_alpha: 7.876496534341526e-05 and depth of 33 Number of nodes in the tree # 1002 is: 1391 with ccp_alpha: 7.876496534341526e-05 and depth of 33 Number of nodes in the tree # 1003 is: 1389 with ccp_alpha: 7.898375691381366e-05 and depth of 33 Number of nodes in the tree # 1004 is: 1387 with ccp_alpha: 7.921415805427516e-05 and depth of 33 Number of nodes in the tree # 1005 is: 1377 with ccp_alpha: 7.948542891025878e-05 and depth of 33 Number of nodes in the tree # 1006 is: 1375 with ccp_alpha: 8.001520288854883e-05 and depth of 33 Number of nodes in the tree # 1007 is: 1373 with ccp_alpha: 8.009855205822456e-05 and depth of 33 Number of nodes in the tree # 1008 is: 1365 with ccp_alpha: 8.034196951966325e-05 and depth of 33 Number of nodes in the tree # 1009 is: 1361 with ccp_alpha: 8.038659698283843e-05 and depth of 33 Number of nodes in the tree # 1010 is: 1357 with ccp_alpha: 8.061137104900713e-05 and depth of 33 Number of nodes in the tree # 1011 is: 1351 with ccp_alpha: 8.078457983940025e-05 and depth of 33 Number of nodes in the tree # 1012 is: 1347 with ccp_alpha: 8.096217550663436e-05 and depth of 33 Number of nodes in the tree # 1013 is: 1343 with ccp_alpha: 8.116883116883117e-05 and depth of 33 Number of nodes in the tree # 1014 is: 1335 with ccp_alpha: 8.18436752983067e-05 and depth of 33 Number of nodes in the tree # 1015 is: 1315 with ccp_alpha: 8.195217493421653e-05 and depth of 29 Number of nodes in the tree # 1016 is: 1309 with ccp_alpha: 8.199117245137712e-05 and depth of 29 Number of nodes in the tree # 1017 is: 1295 with ccp_alpha: 8.239286439100928e-05 and depth of 29 Number of nodes in the tree # 1018 is: 1277 with ccp_alpha: 8.251610997908777e-05 and depth of 29 Number of nodes in the tree # 1019 is: 1275 with ccp_alpha: 8.267108264568032e-05 and depth of 29 Number of nodes in the tree # 1020 is: 1271 with ccp_alpha: 8.275009751852855e-05 and depth of 29 Number of nodes in the tree # 1021 is: 1261 with ccp_alpha: 8.288843186841653e-05 and depth of 29 Number of nodes in the tree # 1022 is: 1259 with ccp_alpha: 8.294556735010414e-05 and depth of 29 Number of nodes in the tree # 1023 is: 1253 with ccp_alpha: 8.351283100064319e-05 and depth of 29 Number of nodes in the tree # 1024 is: 1239 with ccp_alpha: 8.375251557713275e-05 and depth of 29 Number of nodes in the tree # 1025 is: 1233 with ccp_alpha: 8.375782809126092e-05 and depth of 29 Number of nodes in the tree # 1026 is: 1231 with ccp_alpha: 8.401596303297626e-05 and depth of 29 Number of nodes in the tree # 1027 is: 1229 with ccp_alpha: 8.401596303297628e-05 and depth of 29 Number of nodes in the tree # 1028 is: 1225 with ccp_alpha: 8.405202757754738e-05 and depth of 29 Number of nodes in the tree # 1029 is: 1219 with ccp_alpha: 8.42949296509617e-05 and depth of 29 Number of nodes in the tree # 1030 is: 1217 with ccp_alpha: 8.439103429651632e-05 and depth of 29 Number of nodes in the tree # 1031 is: 1211 with ccp_alpha: 8.47591597959167e-05 and depth of 29 Number of nodes in the tree # 1032 is: 1205 with ccp_alpha: 8.480778014489422e-05 and depth of 29 Number of nodes in the tree # 1033 is: 1203 with ccp_alpha: 8.532871245536652e-05 and depth of 29 Number of nodes in the tree # 1034 is: 1201 with ccp_alpha: 8.552896236725628e-05 and depth of 29 Number of nodes in the tree # 1035 is: 1197 with ccp_alpha: 8.605411766203489e-05 and depth of 29 Number of nodes in the tree # 1036 is: 1193 with ccp_alpha: 8.616409845143308e-05 and depth of 29 Number of nodes in the tree # 1037 is: 1193 with ccp_alpha: 8.616409845143308e-05 and depth of 29 Number of nodes in the tree # 1038 is: 1191 with ccp_alpha: 8.61640984514331e-05 and depth of 29 Number of nodes in the tree # 1039 is: 1185 with ccp_alpha: 8.619893507778311e-05 and depth of 29 Number of nodes in the tree # 1040 is: 1181 with ccp_alpha: 8.623797612455458e-05 and depth of 29 Number of nodes in the tree # 1041 is: 1175 with ccp_alpha: 8.636545422177036e-05 and depth of 29 Number of nodes in the tree # 1042 is: 1171 with ccp_alpha: 8.64099229171339e-05 and depth of 29 Number of nodes in the tree # 1043 is: 1159 with ccp_alpha: 8.661162666361152e-05 and depth of 29 Number of nodes in the tree # 1044 is: 1157 with ccp_alpha: 8.676797129885923e-05 and depth of 29 Number of nodes in the tree # 1045 is: 1155 with ccp_alpha: 8.68128946369842e-05 and depth of 29 Number of nodes in the tree # 1046 is: 1149 with ccp_alpha: 8.776667566837697e-05 and depth of 29 Number of nodes in the tree # 1047 is: 1139 with ccp_alpha: 8.824062935594131e-05 and depth of 29 Number of nodes in the tree # 1048 is: 1131 with ccp_alpha: 8.837081176638409e-05 and depth of 29 Number of nodes in the tree # 1049 is: 1125 with ccp_alpha: 8.841753462569651e-05 and depth of 29 Number of nodes in the tree # 1050 is: 1123 with ccp_alpha: 8.861058601134216e-05 and depth of 29 Number of nodes in the tree # 1051 is: 1121 with ccp_alpha: 8.861717171078555e-05 and depth of 29 Number of nodes in the tree # 1052 is: 1119 with ccp_alpha: 8.895807850550427e-05 and depth of 29 Number of nodes in the tree # 1053 is: 1117 with ccp_alpha: 8.954332902198791e-05 and depth of 29 Number of nodes in the tree # 1054 is: 1109 with ccp_alpha: 8.970370814304985e-05 and depth of 29 Number of nodes in the tree # 1055 is: 1103 with ccp_alpha: 9.001710324961742e-05 and depth of 29 Number of nodes in the tree # 1056 is: 1097 with ccp_alpha: 9.027676797052982e-05 and depth of 29 Number of nodes in the tree # 1057 is: 1085 with ccp_alpha: 9.18645071018778e-05 and depth of 29 Number of nodes in the tree # 1058 is: 1079 with ccp_alpha: 9.21192233002003e-05 and depth of 29 Number of nodes in the tree # 1059 is: 1077 with ccp_alpha: 9.283013772616794e-05 and depth of 29 Number of nodes in the tree # 1060 is: 1073 with ccp_alpha: 9.311232049690466e-05 and depth of 29 Number of nodes in the tree # 1061 is: 1071 with ccp_alpha: 9.365971963173556e-05 and depth of 29 Number of nodes in the tree # 1062 is: 1065 with ccp_alpha: 9.439628180229994e-05 and depth of 29 Number of nodes in the tree # 1063 is: 1063 with ccp_alpha: 9.440563760590475e-05 and depth of 29 Number of nodes in the tree # 1064 is: 1055 with ccp_alpha: 9.451795841209829e-05 and depth of 29 Number of nodes in the tree # 1065 is: 1055 with ccp_alpha: 9.451795841209829e-05 and depth of 29 Number of nodes in the tree # 1066 is: 1055 with ccp_alpha: 9.451795841209829e-05 and depth of 29 Number of nodes in the tree # 1067 is: 1053 with ccp_alpha: 9.472565507527567e-05 and depth of 29 Number of nodes in the tree # 1068 is: 1051 with ccp_alpha: 9.47528829151939e-05 and depth of 29 Number of nodes in the tree # 1069 is: 1047 with ccp_alpha: 9.47776231330107e-05 and depth of 29 Number of nodes in the tree # 1070 is: 1041 with ccp_alpha: 9.520455987568205e-05 and depth of 29 Number of nodes in the tree # 1071 is: 1037 with ccp_alpha: 9.532580421049228e-05 and depth of 29 Number of nodes in the tree # 1072 is: 1035 with ccp_alpha: 9.547268526474576e-05 and depth of 29 Number of nodes in the tree # 1073 is: 1033 with ccp_alpha: 9.572131204928938e-05 and depth of 29 Number of nodes in the tree # 1074 is: 1027 with ccp_alpha: 9.605136941830915e-05 and depth of 29 Number of nodes in the tree # 1075 is: 1021 with ccp_alpha: 9.623076240403338e-05 and depth of 29 Number of nodes in the tree # 1076 is: 1017 with ccp_alpha: 9.647766089911152e-05 and depth of 29 Number of nodes in the tree # 1077 is: 1013 with ccp_alpha: 9.651513274701681e-05 and depth of 29 Number of nodes in the tree # 1078 is: 1005 with ccp_alpha: 9.78036950082237e-05 and depth of 29 Number of nodes in the tree # 1079 is: 1003 with ccp_alpha: 9.821553595183079e-05 and depth of 29 Number of nodes in the tree # 1080 is: 1001 with ccp_alpha: 9.825151602089219e-05 and depth of 29 Number of nodes in the tree # 1081 is: 999 with ccp_alpha: 9.845620667926904e-05 and depth of 29 Number of nodes in the tree # 1082 is: 995 with ccp_alpha: 9.845620667926907e-05 and depth of 29 Number of nodes in the tree # 1083 is: 995 with ccp_alpha: 9.845620667926907e-05 and depth of 29 Number of nodes in the tree # 1084 is: 993 with ccp_alpha: 9.859200834365432e-05 and depth of 29 Number of nodes in the tree # 1085 is: 989 with ccp_alpha: 9.874721024580877e-05 and depth of 29 Number of nodes in the tree # 1086 is: 987 with ccp_alpha: 9.920634920634923e-05 and depth of 29 Number of nodes in the tree # 1087 is: 977 with ccp_alpha: 9.950430316643267e-05 and depth of 29 Number of nodes in the tree # 1088 is: 969 with ccp_alpha: 9.958539695774837e-05 and depth of 29 Number of nodes in the tree # 1089 is: 967 with ccp_alpha: 0.00010007783831869233 and depth of 29 Number of nodes in the tree # 1090 is: 965 with ccp_alpha: 0.00010012319007278025 and depth of 29 Number of nodes in the tree # 1091 is: 955 with ccp_alpha: 0.00010027142225988452 and depth of 29 Number of nodes in the tree # 1092 is: 953 with ccp_alpha: 0.00010036566038456395 and depth of 29 Number of nodes in the tree # 1093 is: 947 with ccp_alpha: 0.00010043574945906387 and depth of 29 Number of nodes in the tree # 1094 is: 925 with ccp_alpha: 0.00010049030405080848 and depth of 29 Number of nodes in the tree # 1095 is: 913 with ccp_alpha: 0.00010056904380698567 and depth of 29 Number of nodes in the tree # 1096 is: 911 with ccp_alpha: 0.00010116219121802272 and depth of 29 Number of nodes in the tree # 1097 is: 909 with ccp_alpha: 0.00010126924115581962 and depth of 29 Number of nodes in the tree # 1098 is: 907 with ccp_alpha: 0.00010141926966123556 and depth of 29 Number of nodes in the tree # 1099 is: 903 with ccp_alpha: 0.00010355276326031358 and depth of 27 Number of nodes in the tree # 1100 is: 883 with ccp_alpha: 0.00010382568325565747 and depth of 27 Number of nodes in the tree # 1101 is: 879 with ccp_alpha: 0.00010385239324503923 and depth of 27 Number of nodes in the tree # 1102 is: 871 with ccp_alpha: 0.00010500818684961908 and depth of 27 Number of nodes in the tree # 1103 is: 865 with ccp_alpha: 0.00010501995379122033 and depth of 27 Number of nodes in the tree # 1104 is: 865 with ccp_alpha: 0.00010501995379122033 and depth of 27 Number of nodes in the tree # 1105 is: 865 with ccp_alpha: 0.00010501995379122033 and depth of 27 Number of nodes in the tree # 1106 is: 861 with ccp_alpha: 0.00010502855122135064 and depth of 27 Number of nodes in the tree # 1107 is: 857 with ccp_alpha: 0.00010504017360485839 and depth of 27 Number of nodes in the tree # 1108 is: 855 with ccp_alpha: 0.00010518265711919293 and depth of 27 Number of nodes in the tree # 1109 is: 853 with ccp_alpha: 0.00010538460640855096 and depth of 27 Number of nodes in the tree # 1110 is: 841 with ccp_alpha: 0.00010539405737864174 and depth of 27 Number of nodes in the tree # 1111 is: 833 with ccp_alpha: 0.0001054205980954563 and depth of 27 Number of nodes in the tree # 1112 is: 831 with ccp_alpha: 0.00010542300765660874 and depth of 27 Number of nodes in the tree # 1113 is: 825 with ccp_alpha: 0.00010576236248204089 and depth of 27 Number of nodes in the tree # 1114 is: 823 with ccp_alpha: 0.00010626524968597792 and depth of 27 Number of nodes in the tree # 1115 is: 821 with ccp_alpha: 0.0001063327032136106 and depth of 27 Number of nodes in the tree # 1116 is: 815 with ccp_alpha: 0.00010637544848936312 and depth of 27 Number of nodes in the tree # 1117 is: 811 with ccp_alpha: 0.00010648765384191323 and depth of 27 Number of nodes in the tree # 1118 is: 809 with ccp_alpha: 0.00010654079069543796 and depth of 27 Number of nodes in the tree # 1119 is: 795 with ccp_alpha: 0.00010722456330783578 and depth of 27 Number of nodes in the tree # 1120 is: 789 with ccp_alpha: 0.00010805180004239597 and depth of 27 Number of nodes in the tree # 1121 is: 773 with ccp_alpha: 0.00010815138122479597 and depth of 27 Number of nodes in the tree # 1122 is: 761 with ccp_alpha: 0.00010833138268128357 and depth of 27 Number of nodes in the tree # 1123 is: 759 with ccp_alpha: 0.00010841133374491798 and depth of 27 Number of nodes in the tree # 1124 is: 753 with ccp_alpha: 0.00010959424948082796 and depth of 27 Number of nodes in the tree # 1125 is: 747 with ccp_alpha: 0.00011009874895097993 and depth of 27 Number of nodes in the tree # 1126 is: 743 with ccp_alpha: 0.00011027344918064947 and depth of 27 Number of nodes in the tree # 1127 is: 739 with ccp_alpha: 0.00011092732619197651 and depth of 27 Number of nodes in the tree # 1128 is: 733 with ccp_alpha: 0.00011104315702336384 and depth of 27 Number of nodes in the tree # 1129 is: 731 with ccp_alpha: 0.00011129832059395637 and depth of 27 Number of nodes in the tree # 1130 is: 727 with ccp_alpha: 0.00011130733260372098 and depth of 27 Number of nodes in the tree # 1131 is: 723 with ccp_alpha: 0.00011202128404396835 and depth of 27 Number of nodes in the tree # 1132 is: 717 with ccp_alpha: 0.0001124566013172014 and depth of 27 Number of nodes in the tree # 1133 is: 715 with ccp_alpha: 0.00011252137906202179 and depth of 27 Number of nodes in the tree # 1134 is: 711 with ccp_alpha: 0.00011262847305329061 and depth of 27 Number of nodes in the tree # 1135 is: 709 with ccp_alpha: 0.00011270690896511017 and depth of 27 Number of nodes in the tree # 1136 is: 707 with ccp_alpha: 0.00011283393844830516 and depth of 27 Number of nodes in the tree # 1137 is: 705 with ccp_alpha: 0.00011309841177516036 and depth of 27 Number of nodes in the tree # 1138 is: 701 with ccp_alpha: 0.00011341951072544958 and depth of 27 Number of nodes in the tree # 1139 is: 695 with ccp_alpha: 0.000113535168173181 and depth of 27 Number of nodes in the tree # 1140 is: 675 with ccp_alpha: 0.00011377387702089528 and depth of 27 Number of nodes in the tree # 1141 is: 673 with ccp_alpha: 0.00011410062648745369 and depth of 27 Number of nodes in the tree # 1142 is: 671 with ccp_alpha: 0.00011524917006958601 and depth of 27 Number of nodes in the tree # 1143 is: 663 with ccp_alpha: 0.00011534192665463099 and depth of 27 Number of nodes in the tree # 1144 is: 659 with ccp_alpha: 0.00011585619270934253 and depth of 27 Number of nodes in the tree # 1145 is: 631 with ccp_alpha: 0.00011632014003514709 and depth of 21 Number of nodes in the tree # 1146 is: 627 with ccp_alpha: 0.00011736595788977046 and depth of 21 Number of nodes in the tree # 1147 is: 625 with ccp_alpha: 0.00011740337951660209 and depth of 21 Number of nodes in the tree # 1148 is: 615 with ccp_alpha: 0.00011799258961568554 and depth of 21 Number of nodes in the tree # 1149 is: 613 with ccp_alpha: 0.00011821608850779688 and depth of 21 Number of nodes in the tree # 1150 is: 607 with ccp_alpha: 0.00011946764720555114 and depth of 21 Number of nodes in the tree # 1151 is: 603 with ccp_alpha: 0.00011992733164755846 and depth of 21 Number of nodes in the tree # 1152 is: 599 with ccp_alpha: 0.00012013311941033494 and depth of 21 Number of nodes in the tree # 1153 is: 597 with ccp_alpha: 0.00012038603124067253 and depth of 21 Number of nodes in the tree # 1154 is: 595 with ccp_alpha: 0.00012072675039929138 and depth of 21 Number of nodes in the tree # 1155 is: 593 with ccp_alpha: 0.00012081860770937788 and depth of 21 Number of nodes in the tree # 1156 is: 591 with ccp_alpha: 0.00012111102524782653 and depth of 21 Number of nodes in the tree # 1157 is: 575 with ccp_alpha: 0.00012125389662507745 and depth of 20 Number of nodes in the tree # 1158 is: 573 with ccp_alpha: 0.00012148398260173135 and depth of 20 Number of nodes in the tree # 1159 is: 571 with ccp_alpha: 0.00012204695466561761 and depth of 20 Number of nodes in the tree # 1160 is: 569 with ccp_alpha: 0.00012252327942309038 and depth of 20 Number of nodes in the tree # 1161 is: 567 with ccp_alpha: 0.0001235740794181424 and depth of 20 Number of nodes in the tree # 1162 is: 565 with ccp_alpha: 0.00012463906603793192 and depth of 20 Number of nodes in the tree # 1163 is: 563 with ccp_alpha: 0.0001258997830277408 and depth of 20 Number of nodes in the tree # 1164 is: 561 with ccp_alpha: 0.00012638597857657103 and depth of 20 Number of nodes in the tree # 1165 is: 559 with ccp_alpha: 0.00012805234718734487 and depth of 20 Number of nodes in the tree # 1166 is: 557 with ccp_alpha: 0.0001282315600261817 and depth of 20 Number of nodes in the tree # 1167 is: 555 with ccp_alpha: 0.0001288881251074068 and depth of 20 Number of nodes in the tree # 1168 is: 553 with ccp_alpha: 0.0001295222685971523 and depth of 20 Number of nodes in the tree # 1169 is: 551 with ccp_alpha: 0.00013076865409381326 and depth of 20 Number of nodes in the tree # 1170 is: 549 with ccp_alpha: 0.00013127494223902543 and depth of 20 Number of nodes in the tree # 1171 is: 547 with ccp_alpha: 0.00013158164237025462 and depth of 20 Number of nodes in the tree # 1172 is: 543 with ccp_alpha: 0.00013428058019479853 and depth of 20 Number of nodes in the tree # 1173 is: 541 with ccp_alpha: 0.00013492536844032451 and depth of 20 Number of nodes in the tree # 1174 is: 527 with ccp_alpha: 0.0001353416284865287 and depth of 18 Number of nodes in the tree # 1175 is: 525 with ccp_alpha: 0.0001354944939538512 and depth of 18 Number of nodes in the tree # 1176 is: 521 with ccp_alpha: 0.00013608378576119196 and depth of 18 Number of nodes in the tree # 1177 is: 519 with ccp_alpha: 0.00013613808214469844 and depth of 18 Number of nodes in the tree # 1178 is: 517 with ccp_alpha: 0.00013631622266295149 and depth of 18 Number of nodes in the tree # 1179 is: 513 with ccp_alpha: 0.00013744419304117855 and depth of 18 Number of nodes in the tree # 1180 is: 501 with ccp_alpha: 0.0001398542581859403 and depth of 16 Number of nodes in the tree # 1181 is: 497 with ccp_alpha: 0.00014032838653137212 and depth of 16 Number of nodes in the tree # 1182 is: 495 with ccp_alpha: 0.00014089633552114033 and depth of 16 Number of nodes in the tree # 1183 is: 493 with ccp_alpha: 0.0001409478327197957 and depth of 16 Number of nodes in the tree # 1184 is: 491 with ccp_alpha: 0.00014177217925763312 and depth of 16 Number of nodes in the tree # 1185 is: 489 with ccp_alpha: 0.0001417888717038055 and depth of 16 Number of nodes in the tree # 1186 is: 487 with ccp_alpha: 0.00014188057573044156 and depth of 16 Number of nodes in the tree # 1187 is: 485 with ccp_alpha: 0.0001422596747415781 and depth of 16 Number of nodes in the tree # 1188 is: 483 with ccp_alpha: 0.00014229680209528958 and depth of 16 Number of nodes in the tree # 1189 is: 477 with ccp_alpha: 0.00014278547272575468 and depth of 16 Number of nodes in the tree # 1190 is: 475 with ccp_alpha: 0.00014294382599360552 and depth of 16 Number of nodes in the tree # 1191 is: 473 with ccp_alpha: 0.00014320902789711866 and depth of 16 Number of nodes in the tree # 1192 is: 465 with ccp_alpha: 0.00014336558080650717 and depth of 16 Number of nodes in the tree # 1193 is: 463 with ccp_alpha: 0.00014418941424428446 and depth of 16 Number of nodes in the tree # 1194 is: 459 with ccp_alpha: 0.00014550466162611356 and depth of 16 Number of nodes in the tree # 1195 is: 453 with ccp_alpha: 0.0001460430386972302 and depth of 16 Number of nodes in the tree # 1196 is: 451 with ccp_alpha: 0.00014634036786504107 and depth of 16 Number of nodes in the tree # 1197 is: 449 with ccp_alpha: 0.00014781217522238314 and depth of 16 Number of nodes in the tree # 1198 is: 447 with ccp_alpha: 0.0001482634641758405 and depth of 16 Number of nodes in the tree # 1199 is: 445 with ccp_alpha: 0.00014841790528435694 and depth of 16 Number of nodes in the tree # 1200 is: 443 with ccp_alpha: 0.0001485282203618687 and depth of 16 Number of nodes in the tree # 1201 is: 441 with ccp_alpha: 0.0001499243445948293 and depth of 16 Number of nodes in the tree # 1202 is: 439 with ccp_alpha: 0.000150106742572496 and depth of 16 Number of nodes in the tree # 1203 is: 437 with ccp_alpha: 0.00015072551284744094 and depth of 16 Number of nodes in the tree # 1204 is: 435 with ccp_alpha: 0.0001526036314724033 and depth of 16 Number of nodes in the tree # 1205 is: 433 with ccp_alpha: 0.00015275629642359326 and depth of 16 Number of nodes in the tree # 1206 is: 431 with ccp_alpha: 0.00015343824417548424 and depth of 16 Number of nodes in the tree # 1207 is: 417 with ccp_alpha: 0.00015398600447623815 and depth of 16 Number of nodes in the tree # 1208 is: 415 with ccp_alpha: 0.00015504914437680143 and depth of 16 Number of nodes in the tree # 1209 is: 411 with ccp_alpha: 0.00015932725754774432 and depth of 16 Number of nodes in the tree # 1210 is: 399 with ccp_alpha: 0.00015991820500685732 and depth of 16 Number of nodes in the tree # 1211 is: 397 with ccp_alpha: 0.00016073548160196944 and depth of 16 Number of nodes in the tree # 1212 is: 389 with ccp_alpha: 0.00016180322978890532 and depth of 16 Number of nodes in the tree # 1213 is: 385 with ccp_alpha: 0.00016260574852605594 and depth of 16 Number of nodes in the tree # 1214 is: 383 with ccp_alpha: 0.00016358877417478555 and depth of 16 Number of nodes in the tree # 1215 is: 381 with ccp_alpha: 0.00016540642722117204 and depth of 16 Number of nodes in the tree # 1216 is: 377 with ccp_alpha: 0.00016712378402135633 and depth of 16 Number of nodes in the tree # 1217 is: 375 with ccp_alpha: 0.00016737814572120478 and depth of 16 Number of nodes in the tree # 1218 is: 373 with ccp_alpha: 0.0001683404643272636 and depth of 16 Number of nodes in the tree # 1219 is: 367 with ccp_alpha: 0.00016838947407630538 and depth of 16 Number of nodes in the tree # 1220 is: 361 with ccp_alpha: 0.00016925536161630427 and depth of 16 Number of nodes in the tree # 1221 is: 357 with ccp_alpha: 0.00016940530369592092 and depth of 16 Number of nodes in the tree # 1222 is: 351 with ccp_alpha: 0.00017237650153529186 and depth of 16 Number of nodes in the tree # 1223 is: 349 with ccp_alpha: 0.00017262008069374342 and depth of 16 Number of nodes in the tree # 1224 is: 345 with ccp_alpha: 0.00017301847400025576 and depth of 16 Number of nodes in the tree # 1225 is: 341 with ccp_alpha: 0.00017368684665471052 and depth of 16 Number of nodes in the tree # 1226 is: 339 with ccp_alpha: 0.00017374425856893534 and depth of 16 Number of nodes in the tree # 1227 is: 337 with ccp_alpha: 0.00017577217789863098 and depth of 16 Number of nodes in the tree # 1228 is: 335 with ccp_alpha: 0.0001757813072635274 and depth of 16 Number of nodes in the tree # 1229 is: 333 with ccp_alpha: 0.00017657817528098172 and depth of 16 Number of nodes in the tree # 1230 is: 331 with ccp_alpha: 0.00017671626839868803 and depth of 16 Number of nodes in the tree # 1231 is: 327 with ccp_alpha: 0.00017722117202268432 and depth of 16 Number of nodes in the tree # 1232 is: 327 with ccp_alpha: 0.00017722117202268432 and depth of 16 Number of nodes in the tree # 1233 is: 325 with ccp_alpha: 0.00017823968450557393 and depth of 16 Number of nodes in the tree # 1234 is: 323 with ccp_alpha: 0.00018024594629228834 and depth of 16 Number of nodes in the tree # 1235 is: 313 with ccp_alpha: 0.0001823767602741444 and depth of 16 Number of nodes in the tree # 1236 is: 311 with ccp_alpha: 0.00018378491913463566 and depth of 15 Number of nodes in the tree # 1237 is: 309 with ccp_alpha: 0.00018446246722361118 and depth of 15 Number of nodes in the tree # 1238 is: 305 with ccp_alpha: 0.00018606218188731063 and depth of 15 Number of nodes in the tree # 1239 is: 303 with ccp_alpha: 0.00018914070391777983 and depth of 15 Number of nodes in the tree # 1240 is: 301 with ccp_alpha: 0.0001900678642311023 and depth of 15 Number of nodes in the tree # 1241 is: 297 with ccp_alpha: 0.00019015329941166406 and depth of 15 Number of nodes in the tree # 1242 is: 295 with ccp_alpha: 0.00019347755020822388 and depth of 15 Number of nodes in the tree # 1243 is: 291 with ccp_alpha: 0.00020260292650878318 and depth of 14 Number of nodes in the tree # 1244 is: 289 with ccp_alpha: 0.00020531074507002152 and depth of 14 Number of nodes in the tree # 1245 is: 287 with ccp_alpha: 0.00020537235408060872 and depth of 14 Number of nodes in the tree # 1246 is: 285 with ccp_alpha: 0.0002103898910622232 and depth of 14 Number of nodes in the tree # 1247 is: 283 with ccp_alpha: 0.0002133433374513936 and depth of 14 Number of nodes in the tree # 1248 is: 281 with ccp_alpha: 0.00021384574738298628 and depth of 14 Number of nodes in the tree # 1249 is: 273 with ccp_alpha: 0.00021545523332150906 and depth of 14 Number of nodes in the tree # 1250 is: 269 with ccp_alpha: 0.00021842602182866819 and depth of 14 Number of nodes in the tree # 1251 is: 267 with ccp_alpha: 0.00022047299757974557 and depth of 14 Number of nodes in the tree # 1252 is: 259 with ccp_alpha: 0.00022536630276519427 and depth of 14 Number of nodes in the tree # 1253 is: 255 with ccp_alpha: 0.00022653810888748115 and depth of 14 Number of nodes in the tree # 1254 is: 253 with ccp_alpha: 0.0002336662680536613 and depth of 14 Number of nodes in the tree # 1255 is: 251 with ccp_alpha: 0.00023441953971254536 and depth of 14 Number of nodes in the tree # 1256 is: 249 with ccp_alpha: 0.00023522879759876186 and depth of 14 Number of nodes in the tree # 1257 is: 245 with ccp_alpha: 0.00023636002128029731 and depth of 14 Number of nodes in the tree # 1258 is: 239 with ccp_alpha: 0.00024075520139078357 and depth of 14 Number of nodes in the tree # 1259 is: 237 with ccp_alpha: 0.00024288164914013435 and depth of 14 Number of nodes in the tree # 1260 is: 235 with ccp_alpha: 0.0002448436410831135 and depth of 14 Number of nodes in the tree # 1261 is: 233 with ccp_alpha: 0.0002501865003857852 and depth of 14 Number of nodes in the tree # 1262 is: 229 with ccp_alpha: 0.00025442465647420394 and depth of 13 Number of nodes in the tree # 1263 is: 221 with ccp_alpha: 0.0002568006678810991 and depth of 13 Number of nodes in the tree # 1264 is: 219 with ccp_alpha: 0.0002592759117984564 and depth of 13 Number of nodes in the tree # 1265 is: 215 with ccp_alpha: 0.00026976921296216743 and depth of 13 Number of nodes in the tree # 1266 is: 213 with ccp_alpha: 0.0002735720258547217 and depth of 13 Number of nodes in the tree # 1267 is: 211 with ccp_alpha: 0.000279026116739238 and depth of 13 Number of nodes in the tree # 1268 is: 205 with ccp_alpha: 0.00028579387211843883 and depth of 13 Number of nodes in the tree # 1269 is: 203 with ccp_alpha: 0.0002860488515627655 and depth of 13 Number of nodes in the tree # 1270 is: 201 with ccp_alpha: 0.0002922344946451581 and depth of 13 Number of nodes in the tree # 1271 is: 199 with ccp_alpha: 0.0002952115925949375 and depth of 13 Number of nodes in the tree # 1272 is: 195 with ccp_alpha: 0.0002982828378235063 and depth of 13 Number of nodes in the tree # 1273 is: 193 with ccp_alpha: 0.0003179234873645154 and depth of 13 Number of nodes in the tree # 1274 is: 189 with ccp_alpha: 0.0003246223807033006 and depth of 13 Number of nodes in the tree # 1275 is: 187 with ccp_alpha: 0.000326476865769333 and depth of 13 Number of nodes in the tree # 1276 is: 183 with ccp_alpha: 0.00034030766157890774 and depth of 13 Number of nodes in the tree # 1277 is: 179 with ccp_alpha: 0.0003502421802736907 and depth of 13 Number of nodes in the tree # 1278 is: 177 with ccp_alpha: 0.00035887217242372405 and depth of 13 Number of nodes in the tree # 1279 is: 175 with ccp_alpha: 0.00036809181692871746 and depth of 13 Number of nodes in the tree # 1280 is: 173 with ccp_alpha: 0.00037339441740058023 and depth of 13 Number of nodes in the tree # 1281 is: 171 with ccp_alpha: 0.0004022166207629666 and depth of 13 Number of nodes in the tree # 1282 is: 169 with ccp_alpha: 0.00042088556624679876 and depth of 13 Number of nodes in the tree # 1283 is: 167 with ccp_alpha: 0.0004240539745104762 and depth of 13 Number of nodes in the tree # 1284 is: 163 with ccp_alpha: 0.00042698709711536543 and depth of 13 Number of nodes in the tree # 1285 is: 161 with ccp_alpha: 0.00044410700549150526 and depth of 13 Number of nodes in the tree # 1286 is: 159 with ccp_alpha: 0.00046274417139256476 and depth of 13 Number of nodes in the tree # 1287 is: 157 with ccp_alpha: 0.00048546418214213223 and depth of 13 Number of nodes in the tree # 1288 is: 153 with ccp_alpha: 0.0004877401982383491 and depth of 13 Number of nodes in the tree # 1289 is: 147 with ccp_alpha: 0.0004895739149983168 and depth of 11 Number of nodes in the tree # 1290 is: 145 with ccp_alpha: 0.0004919910143134261 and depth of 11 Number of nodes in the tree # 1291 is: 141 with ccp_alpha: 0.0005040616703060746 and depth of 11 Number of nodes in the tree # 1292 is: 137 with ccp_alpha: 0.0005138607239333704 and depth of 11 Number of nodes in the tree # 1293 is: 131 with ccp_alpha: 0.0005446776137230334 and depth of 11 Number of nodes in the tree # 1294 is: 123 with ccp_alpha: 0.0005794946078111624 and depth of 10 Number of nodes in the tree # 1295 is: 119 with ccp_alpha: 0.0005826733094905979 and depth of 10 Number of nodes in the tree # 1296 is: 117 with ccp_alpha: 0.000586274370138451 and depth of 10 Number of nodes in the tree # 1297 is: 115 with ccp_alpha: 0.000589027452583493 and depth of 10 Number of nodes in the tree # 1298 is: 113 with ccp_alpha: 0.0005974621598986095 and depth of 10 Number of nodes in the tree # 1299 is: 105 with ccp_alpha: 0.0006611670247552492 and depth of 10 Number of nodes in the tree # 1300 is: 103 with ccp_alpha: 0.0006614000565377254 and depth of 10 Number of nodes in the tree # 1301 is: 101 with ccp_alpha: 0.0006638655679882532 and depth of 10 Number of nodes in the tree # 1302 is: 99 with ccp_alpha: 0.0006750874406903397 and depth of 10 Number of nodes in the tree # 1303 is: 97 with ccp_alpha: 0.0006755374842731682 and depth of 10 Number of nodes in the tree # 1304 is: 93 with ccp_alpha: 0.0006862570627079873 and depth of 10 Number of nodes in the tree # 1305 is: 87 with ccp_alpha: 0.0007065827675873821 and depth of 9 Number of nodes in the tree # 1306 is: 83 with ccp_alpha: 0.0007069009753010184 and depth of 9 Number of nodes in the tree # 1307 is: 79 with ccp_alpha: 0.0007656841771604252 and depth of 9 Number of nodes in the tree # 1308 is: 75 with ccp_alpha: 0.0008305291372585671 and depth of 9 Number of nodes in the tree # 1309 is: 73 with ccp_alpha: 0.0010390943583218609 and depth of 9 Number of nodes in the tree # 1310 is: 71 with ccp_alpha: 0.001101652994183554 and depth of 9 Number of nodes in the tree # 1311 is: 69 with ccp_alpha: 0.0011452895754004433 and depth of 9 Number of nodes in the tree # 1312 is: 63 with ccp_alpha: 0.0011590380946784641 and depth of 9 Number of nodes in the tree # 1313 is: 55 with ccp_alpha: 0.001260720564902275 and depth of 9 Number of nodes in the tree # 1314 is: 53 with ccp_alpha: 0.0013077574362383992 and depth of 9 Number of nodes in the tree # 1315 is: 49 with ccp_alpha: 0.0013421854075426395 and depth of 9 Number of nodes in the tree # 1316 is: 45 with ccp_alpha: 0.001426236148802699 and depth of 7 Number of nodes in the tree # 1317 is: 43 with ccp_alpha: 0.0014478139683678955 and depth of 7 Number of nodes in the tree # 1318 is: 41 with ccp_alpha: 0.00148866521981382 and depth of 7 Number of nodes in the tree # 1319 is: 39 with ccp_alpha: 0.00152784597552744 and depth of 6 Number of nodes in the tree # 1320 is: 37 with ccp_alpha: 0.0015717410846286077 and depth of 6 Number of nodes in the tree # 1321 is: 35 with ccp_alpha: 0.0017137855412081775 and depth of 6 Number of nodes in the tree # 1322 is: 33 with ccp_alpha: 0.0018095012523194343 and depth of 6 Number of nodes in the tree # 1323 is: 31 with ccp_alpha: 0.0019896155893582047 and depth of 6 Number of nodes in the tree # 1324 is: 29 with ccp_alpha: 0.0020249670524787183 and depth of 6 Number of nodes in the tree # 1325 is: 27 with ccp_alpha: 0.0024063267673031788 and depth of 6 Number of nodes in the tree # 1326 is: 25 with ccp_alpha: 0.002502843949930977 and depth of 6 Number of nodes in the tree # 1327 is: 23 with ccp_alpha: 0.003007294153688861 and depth of 6 Number of nodes in the tree # 1328 is: 21 with ccp_alpha: 0.0031728826341325643 and depth of 5 Number of nodes in the tree # 1329 is: 19 with ccp_alpha: 0.003313782908012745 and depth of 5 Number of nodes in the tree # 1330 is: 17 with ccp_alpha: 0.004878708414506029 and depth of 5 Number of nodes in the tree # 1331 is: 15 with ccp_alpha: 0.005549877878634545 and depth of 4 Number of nodes in the tree # 1332 is: 11 with ccp_alpha: 0.006665683999430263 and depth of 4 Number of nodes in the tree # 1333 is: 9 with ccp_alpha: 0.013044802205758793 and depth of 3 Number of nodes in the tree # 1334 is: 7 with ccp_alpha: 0.01725993106102921 and depth of 3 Number of nodes in the tree # 1335 is: 3 with ccp_alpha: 0.02399047944509565 and depth of 1 Number of nodes in the tree # 1336 is: 1 with ccp_alpha: 0.07657789477371368 and depth of 0
# For the remainder, we remove the last element in clfs and ccp_alphas, because it is the trivial tree with only one node.
# Here we show that the number of nodes and tree depth decreases as alpha increases.
clfs = clfs[:-1]
ccp_alphas = ccp_alphas[:-1]
# get node counts and depth of each possible tree
node_counts = [clf.tree_.node_count for clf in clfs]
depth = [clf.tree_.max_depth for clf in clfs]
# plotting Number of nodes vs alpha and Depth vs alpha
fig, ax = plt.subplots(2, 1, figsize=(10, 7))
ax[0].plot(ccp_alphas, node_counts, marker="o", drawstyle="steps-post")
ax[0].set_xlabel("alpha")
ax[0].set_ylabel("number of nodes")
ax[0].set_title("Number of nodes vs alpha")
ax[1].plot(ccp_alphas, depth, marker="o", drawstyle="steps-post")
ax[1].set_xlabel("alpha")
ax[1].set_ylabel("depth of tree")
ax[1].set_title("Depth vs alpha")
fig.tight_layout()
When ccp_alpha is set to zero and keeping the other default parameters of DecisionTreeClassifier, the tree overfits. As alpha increases, more of the tree is pruned, thus creating a decision tree that generalizes better.
# Get train and test accuracy scores for each possible tree.
train_accuracy_scores = [clf.score(X_train,y_train) for clf in clfs]
test_accuracy_scores = [clf.score(X_test,y_test) for clf in clfs]
# plotting Accuracy vs alpha for training and testing sets
fig, ax = plt.subplots(figsize=(10,5))
ax.set_xlabel("alpha")
ax.set_ylabel("accuracy")
ax.set_title("Accuracy vs alpha for training and testing sets")
ax.plot(ccp_alphas, train_accuracy_scores, marker='o', label="train",
drawstyle="steps-post")
ax.plot(ccp_alphas, test_accuracy_scores, marker='o', label="test",
drawstyle="steps-post")
ax.legend()
plt.show()
index_best_model_accuracy = np.argmax(test_accuracy_scores)
best_model_accuracy = clfs[index_best_model_accuracy]
print(best_model_accuracy)
print('Training accuracy of best model: ',best_model_accuracy.score(X_train, y_train))
print('Test accuracy of best model: ',best_model_accuracy.score(X_test, y_test))
DecisionTreeClassifier(ccp_alpha=0.00011799258961568554, random_state=1) Training accuracy of best model: 0.9016225582860744 Test accuracy of best model: 0.8832123495359735
Since accuracy isn't the metric we are looking for, we would try to find a model with high recall
# get the training recall for each possible tree
recall_train = []
for clf in clfs:
y_train_pred = clf.predict(X_train)
recall_value_train = recall_score(y_train,y_train_pred)
recall_train.append(recall_value_train)
# get the training recall for each possible tree
recall_test = []
for clf in clfs:
y_test_pred = clf.predict(X_test)
recall_value_test = recall_score(y_test,y_test_pred)
recall_test.append(recall_value_test)
fig, ax = plt.subplots(figsize=(15, 5))
ax.set_xlabel("alpha")
ax.set_ylabel("Recall")
ax.set_title("Recall vs alpha for training and testing sets")
ax.plot(ccp_alphas, recall_train, marker="o", label="train", drawstyle="steps-post")
ax.plot(ccp_alphas, recall_test, marker="o", label="test", drawstyle="steps-post")
ax.legend()
plt.show()
# creating the model where we get highest train and test recall
index_best_model = np.argmax(recall_test)
best_model = clfs[index_best_model]
print(best_model)
DecisionTreeClassifier(ccp_alpha=2.4891092944023008e-05, random_state=1)
This dataset is giving highest recall for low alpha values, which is unusual. Maximum value of Recall is at 0.000024 alpha, but if we choose decision tree will have a thousand nodes. We would lose the buisness rules and the tree will be hard to understand, instead we can choose alpha 0.00666 and get a good recall.**
# creating the model where we get highest test recall
index_best_model_recall = 1331 # I got this from the step where I printed all the trees.
print("index_best_model_recall:",index_best_model_recall)
Hotel_Booking_Best_Cost_Comp_Tree_Model = clfs[index_best_model_recall]
print(Hotel_Booking_Best_Cost_Comp_Tree_Model)
print('Training recall of best model: ',recall_train[index_best_model_recall])
print('Test recall of best model: ',recall_test[index_best_model_recall])
index_best_model_recall: 1331 DecisionTreeClassifier(ccp_alpha=0.006665683999430263, random_state=1) Training recall of best model: 0.7239029056558651 Test recall of best model: 0.7237365133446905
# creating confusion matrix
confusion_matrix_sklearn(Hotel_Booking_Best_Cost_Comp_Tree_Model,X_train,y_train)
# checking model performance for this model
Hotel_Booking_Best_Cost_Comp_Tree_Model_Train_df = model_performance_classification_sklearnmodels(
Hotel_Booking_Best_Cost_Comp_Tree_Model
,X_train
,y_train
)
Hotel_Booking_Best_Cost_Comp_Tree_Model_Train_df
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.799307 | 0.723903 | 0.684764 | 0.70379 |
# creating confusion matrix
confusion_matrix_sklearn(Hotel_Booking_Best_Cost_Comp_Tree_Model,X_test,y_test)
# checking model performance for this model
Hotel_Booking_Best_Cost_Comp_Tree_Model_Test_df = model_performance_classification_sklearnmodels(
Hotel_Booking_Best_Cost_Comp_Tree_Model
,X_test
,y_test
)
Hotel_Booking_Best_Cost_Comp_Tree_Model_Test_df
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.800239 | 0.723737 | 0.679733 | 0.701045 |
plt.figure(figsize=(15,10))
tree.plot_tree(Hotel_Booking_Best_Cost_Comp_Tree_Model
,feature_names=feature_names
,filled=True
,fontsize=9
,node_ids=True
,class_names=True
)
plt.show()
# Text report showing the rules of a decision tree
print(tree.export_text(Hotel_Booking_Best_Cost_Comp_Tree_Model
,feature_names=feature_names
,show_weights=True
)
)
|--- lead_time <= 151.50 | |--- no_of_special_requests <= 0.50 | | |--- market_segment_type_Online <= 0.50 | | | |--- weights: [4614.00, 781.00] class: 0 | | |--- market_segment_type_Online > 0.50 | | | |--- lead_time <= 13.50 | | | | |--- weights: [1085.00, 328.00] class: 0 | | | |--- lead_time > 13.50 | | | | |--- weights: [1419.00, 2440.00] class: 1 | |--- no_of_special_requests > 0.50 | | |--- weights: [8543.00, 1200.00] class: 0 |--- lead_time > 151.50 | |--- avg_price_per_room <= 100.04 | | |--- weights: [1280.00, 1491.00] class: 1 | |--- avg_price_per_room > 100.04 | | |--- weights: [88.00, 2123.00] class: 1
# importance of features in the tree building ( The importance of a feature is computed as the
# (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance)
pd.DataFrame(Hotel_Booking_Best_Cost_Comp_Tree_Model.feature_importances_
,columns=['Importance']
,index=X.columns
).sort_values(by='Importance'
,ascending=False
)
| Importance | |
|---|---|
| lead_time | 0.578720 |
| market_segment_type_Online | 0.196111 |
| no_of_special_requests | 0.113716 |
| avg_price_per_room | 0.111452 |
| repeated_guest | 0.000000 |
| market_segment_type_Offline | 0.000000 |
| market_segment_type_Corporate | 0.000000 |
| market_segment_type_Complementary | 0.000000 |
| no_of_previous_bookings_not_canceled | 0.000000 |
| no_of_previous_cancellations | 0.000000 |
| no_of_adults | 0.000000 |
| no_of_children | 0.000000 |
| arrival_month | 0.000000 |
| arrival_year | 0.000000 |
| room_type_reserved | 0.000000 |
| required_car_parking_space | 0.000000 |
| type_of_meal_plan | 0.000000 |
| no_of_week_nights | 0.000000 |
| no_of_weekend_nights | 0.000000 |
| arrival_date | 0.000000 |
# plot the importance of features in the tree building in a horizontal bar plot
feature_importances = Hotel_Booking_Best_Cost_Comp_Tree_Model.feature_importances_
indices = np.argsort(feature_importances)
# Set the figure size and title for the plot
plt.figure(figsize=(8, 8))
plt.title("Feature Importances")
# Display a horizontal bar plot (barh) for importance of features
plt.barh(range(len(indices)), feature_importances[indices], color="pink", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()
# training performance comparison
hotel_booking_dec_tree_models_train_compare_df = pd.concat(
[
Hotel_Booking_Sklearn_Tree_Model_Train_df.T,
Hotel_Booking_Hyperparam_Tuning_Tree_Model_Train_df.T,
Hotel_Booking_Best_Cost_Comp_Tree_Model_Train_df.T,
],axis=1
)
hotel_booking_dec_tree_models_train_compare_df.columns=[
"Decision Tree-Sklearn",
"Decision Tree(Pre-Pruning)-using GridSearch for Hyperparameter tuning",
"Decision Tree(Post-Pruning)-Cost Complexity Pruning"
]
print("Training performance comparison:")
hotel_booking_dec_tree_models_train_compare_df
Training performance comparison:
| Decision Tree-Sklearn | Decision Tree(Pre-Pruning)-using GridSearch for Hyperparameter tuning | Decision Tree(Post-Pruning)-Cost Complexity Pruning | |
|---|---|---|---|
| Accuracy | 0.994211 | 0.769494 | 0.799307 |
| Recall | 0.986608 | 0.763123 | 0.723903 |
| Precision | 0.995776 | 0.622391 | 0.684764 |
| F1 | 0.991171 | 0.685610 | 0.703790 |
# testing performance comparison
hotel_booking_dec_tree_models_test_compare_df = pd.concat(
[
Hotel_Booking_Sklearn_Tree_Model_Test_df.T,
Hotel_Booking_Hyperparam_Tuning_Tree_Model_Test_df.T,
Hotel_Booking_Best_Cost_Comp_Tree_Model_Test_df.T,
],axis=1
)
hotel_booking_dec_tree_models_test_compare_df.columns=[
"Decision Tree-Sklearn",
"Decision Tree(Pre-Pruning)-using GridSearch for Hyperparameter tuning",
"Decision Tree(Post-Pruning)-Cost Complexity Pruning"
]
print("Testing performance comparison:")
hotel_booking_dec_tree_models_test_compare_df
Testing performance comparison:
| Decision Tree-Sklearn | Decision Tree(Pre-Pruning)-using GridSearch for Hyperparameter tuning | Decision Tree(Post-Pruning)-Cost Complexity Pruning | |
|---|---|---|---|
| Accuracy | 0.872921 | 0.771938 | 0.800239 |
| Recall | 0.812039 | 0.766894 | 0.723737 |
| Precision | 0.798660 | 0.619211 | 0.679733 |
| F1 | 0.805294 | 0.685185 | 0.701045 |